Traceable Measurement System Design

type: archetype

source: generated

batch: Batch 19

slug: measurement

slug: calibration

slug: context

slug: scale

slug: observability

slug: validation

slug: uncertainty

slug: invariance

components: Repeatability and Reproducibility Profile

mechanisms: Gauge Repeatability and Reproducibility Study

essence_one_line: Define exactly what attribute is being measured, anchor it to a unit and frame, realize it through a validated instrument and procedure, and report the result together with uncertainty and traceability.

compression_statement: Traceable Measurement System Design binds one measurand and intended decision to an operational definition, measurement model, unit or scale, reference frame, sampling plan, instrument or rater, procedure, calibration chain, validity evidence, data-reduction rule, uncertainty budget, quality controls, and result format. It treats a number without its conditions, traceability, detection limits, and uncertainty as incomplete evidence; it revises, qualifies, or refuses measurement when the target, method, or use exceeds the validated boundary.

core_intuition: Measurement is not the discovery of a self-labeling number. It is a governed mapping from an attribute in the world to a value on a scale through a model, instrument, procedure, and frame. Every link can add selection, calibration, resolution, interference, rater, transformation, missingness, and interpretation error. By making those links explicit and traceable, users can decide what the result means, compare it appropriately, reproduce it, and know when its uncertainty or validity is insufficient for the intended use.

canonical_formula: result = measurement_model(target, attribute, context, frame, instrument, procedure, sample, calibration, reduction_rule); report value + unit/scale + expanded uncertainty + coverage or confidence basis + traceability + validity boundary + method/version.

structural_problem: A decision requires quantitative or categorized evidence about a target attribute, but the attribute is underspecified, the instrument observes only a proxy, the unit or scale is unclear, sampling misses relevant variation, calibration and reference links are weak, data reduction hides transformations, uncertainty is omitted, or the reported value travels beyond the context in which it is valid. The organization mistakes an output for a measurement because the measurement chain and its limitations are invisible.

trigger_conditions: A property, construct, quantity, state, performance, exposure, or outcome must be represented by a value.

trigger_conditions: More than one instrument, rater, site, mode, or procedure could produce materially different results.

trigger_conditions: The value will be compared with a threshold, baseline, standard, population, or prior measurement.

trigger_conditions: Calibration, traceability, sampling, uncertainty, or reference-frame choices affect the decision.

trigger_conditions: A proxy is used because the target attribute is not directly observable.

trigger_conditions: High-consequence action depends on whether a difference is real, large enough, and valid for the use.

trigger_conditions: Results must remain interpretable after transfer across time, software, laboratories, organizations, or jurisdictions.

symptoms: Reports contain numbers without units, scale definition, method version, or uncertainty.

symptoms: Teams use the same label for different constructs or different labels for the same construct.

symptoms: Instrument precision is quoted as if it were total measurement uncertainty.

symptoms: Calibration certificates exist but do not trace to the exact configuration, range, environment, or time of use.

symptoms: Replicate measurements disagree more than the reported uncertainty predicts.

symptoms: A threshold decision flips when rater, instrument, site, mode, or aggregation rule changes.

symptoms: Proxy movement is treated as target improvement without fidelity evidence.

symptoms: Below-detection, censored, missing, saturated, or invalid values are silently recoded as ordinary numbers.

symptoms: A value valid for one frame, unit, subgroup, or context is compared outside that boundary.

root_tension: The measurement must simplify an attribute into a usable value while preserving enough meaning, uncertainty, context, and traceability that simplification does not become false precision or invalid comparison.

anti_signatures: The construct, instruments, timing, and scoring are already valid, and the only problem is enforcing the same protocol across sites or subjects; use measurement_protocol_standardization.

anti_signatures: The main task is exposing external signals so hidden operational state can be monitored or controlled; use observability_instrumentation.

anti_signatures: A live instrument is already defined and the task is checking calibration without interrupting service; use non_destructive_calibration_check.

anti_signatures: The task is choosing a comparator or control arm for causal interpretation; use control_condition_specification.

anti_signatures: The task is combining multiple already valid measures into a summary; use aggregation_function_design_and_weighting or aggregation_to_manage_complexity.

structural_intervention: Create a versioned measurement chain that starts from a decision-relevant measurand, declares its unit, scale, reference frame, model, sampling and procedure, validates instrument and proxy behavior, establishes calibration traceability, budgets uncertainty, controls drift and deviations, and issues a qualified value-plus-uncertainty result.

action_logic: State the target, attribute or construct, intended use, decision consequence, and required discrimination.

action_logic: Define the measurand operationally, including system boundary, condition, time, population, and reference frame.

action_logic: Choose or construct a unit and scale whose ordering, intervals, origin, transformation, and comparability are explicit.

action_logic: Write the measurement model connecting the target attribute to indications, observations, ratings, or signals.

action_logic: Identify proxies, nuisance influences, interference, confounding, observer effects, and assumptions in that model.

action_logic: Select instrument, sensor, assay, rater, rubric, questionnaire, extraction rule, or computational estimator fit for range and context.

action_logic: Design sampling across time, space, units, cases, conditions, and replicates so relevant variation is represented.

action_logic: Standardize the procedure enough for repeatable realization while recording justified deviations and context.

action_logic: Calibrate against appropriate references and preserve an unbroken traceability and uncertainty chain where relevant.

action_logic: Validate selectivity, sensitivity, specificity, construct fidelity, range, linearity, resolution, stability, and mode equivalence as applicable.

action_logic: Define data capture, correction, reduction, scoring, censoring, detection-limit, missingness, and rounding rules before consequential use.

action_logic: Build an uncertainty budget that combines relevant random and systematic contributions without double counting.

action_logic: Test repeatability, reproducibility, inter-rater or inter-instrument agreement, and robustness across the intended use boundary.

action_logic: Monitor controls, references, blanks, duplicates, drift, environmental conditions, and protocol deviations during use.

action_logic: Report value, unit or scale, uncertainty, coverage basis, method and version, traceability, time, context, flags, and validity boundary together.

action_logic: Refuse, qualify, repeat, recalibrate, redesign, or escalate when uncertainty or validity is inadequate for the intended decision.

decision_rules: Do not select an instrument before the measurand, intended use, and required discrimination are explicit.

decision_rules: Do not report a bare value when unit, scale, frame, method, or uncertainty materially affects interpretation.

decision_rules: Treat resolution, repeatability, accuracy, validity, and total uncertainty as distinct properties.

decision_rules: Do not claim target measurement from a proxy without a maintained target-proxy model and fidelity evidence.

decision_rules: Calibrate across the range, conditions, and configuration in which results will be used; extrapolation requires qualification.

decision_rules: Preserve raw indications and transformation provenance where feasible so corrected or scored results are auditable.

decision_rules: Define below-detection, saturation, censoring, invalid, and missing states separately from zero.

decision_rules: Match uncertainty and validity evidence to the decision stakes; a method adequate for screening may be inadequate for diagnosis or enforcement.

decision_rules: Compare values only when unit, scale, frame, construct, procedure, and uncertainty are sufficiently commensurate.

decision_rules: Suspend or qualify results when controls, calibration, drift, environmental, or reproducibility criteria fail.

decision_rules: Version changes to instruments, software, scoring, reference materials, protocols, and uncertainty models.

decision_rules: Make refusal to measure or report “inconclusive” a valid outcome when the chain cannot support the claim.

target_invariants: The measurand remains explicit and tied to the intended decision throughout the chain.

target_invariants: Unit, scale, origin, ordering, transformations, and reference frame remain interpretable.

target_invariants: Raw indication, corrections, reductions, and final result remain traceable by version and provenance.

target_invariants: Calibration evidence applies to the actual instrument, range, condition, and time of use.

target_invariants: Uncertainty contributions remain visible, justified, and not silently converted into precision.

target_invariants: Proxy results remain labeled as proxy evidence and retain a target-fidelity boundary.

target_invariants: Missing, censored, saturated, invalid, and below-detection states are not silently treated as ordinary values.

target_invariants: Results outside validated range, population, mode, or context are qualified or rejected.

target_invariants: Quality-control failure can stop or flag reporting despite schedule or production pressure.

target_invariants: Historical results remain interpretable after method changes through versioning and crosswalk evidence.

expected_outcomes: Measurements answer a specified question about a defined attribute rather than producing context-free numbers.

expected_outcomes: Results can be compared, repeated, reproduced, and transferred within a documented validity boundary.

expected_outcomes: Decision makers see uncertainty, detection limits, and method limitations alongside the value.

expected_outcomes: Calibration and reference chains are auditable and appropriate to the use.

expected_outcomes: Instrument, rater, sampling, environmental, scoring, and proxy errors are detected earlier.

expected_outcomes: Drift and protocol deviation trigger correction before invalid results accumulate.

expected_outcomes: Method changes preserve continuity through versioned evidence and equivalence testing.

expected_outcomes: High-stakes decisions can distinguish genuine differences from measurement noise or model error.

prose_essence:
Traceable Measurement System Design is the reusable pattern for turning a target attribute into a
defensible value. The value is never separated from what was measured, why, under which conditions,
with which unit or scale, by which instrument and procedure, against which references, with what
uncertainty, and inside what validity boundary. The pattern applies to physical instruments,
laboratory assays, trained raters, questionnaires, computational estimators, telemetry, and mixed
measurement systems.

The intervention begins before instrument selection. It fixes the measurand and intended decision,
defines the scale and reference frame, writes a measurement model, and states required discrimination.
It then selects sampling, instrument, procedure, calibration, validation, and data reduction capable
of supporting that claim. Quality control maintains the chain during use. The final product is a
value-plus-uncertainty evidence object with method, context, traceability, and flags—not a bare number.

prose_when_to_use:
Use this archetype whenever an attribute must be represented as a defensible value for comparison,
monitoring, diagnosis, control, certification, research, or policy. It is especially valuable when
results cross observers, instruments, sites, modes, times, populations, or organizations, or when
small differences have large consequences.

Do not force measurement when the construct lacks a legitimate operational definition, observation
would cause disproportionate harm, or available signals have no defensible relationship to the
target. Qualitative evidence may be more faithful. A number is not automatically superior to a
well-bounded description.

prose_structural_problem:
Targets do not announce their own numeric representation. “Temperature,” “wellbeing,” “response
time,” “pollution,” “quality,” and “performance” each hide choices about system boundary, attribute,
time interval, reference population, scale, instrument response, sample, transformation, and use.
Different defensible choices can produce different values without either instrument being broken.

An indication becomes a measurement only through a model. A sensor voltage must be related to a
quantity; an assay signal to concentration; a survey response to a construct; a trace timestamp to a
latency definition; a rating to a rubric and population. That relationship depends on calibration,
selectivity, nuisance influences, sampling, reduction, and assumptions. If those links are implicit,
the apparent precision of the output conceals uncertainty and model dependence.

Measurement failure often occurs after the number is produced. Results travel to dashboards,
thresholds, contracts, diagnoses, models, and policy decisions without unit, method, uncertainty, or
scope. A change in software, reference material, rater, sampling window, environment, or population
breaks continuity while the same label remains. The archetype prevents that semantic and evidential
separation.

prose_intervention_logic:
Start with the measurand: the specific attribute of a specific target under defined conditions. Link
it to the intended use and the smallest difference or classification that matters. This makes fitness
for purpose concrete. A screening measure may tolerate uncertainty and false positives that would be
unacceptable for diagnosis, enforcement, custody transfer, or safety shutdown.

Define the unit, scale, and frame. For a physical quantity, document unit realization and reference
conditions. For a rating or index, document ordering, interval interpretation, origin, transformations,
and reference population. For telemetry, define event boundaries, clocks, population, aggregation
window, and missing requests. Values cannot be compared merely because their labels match.

Write the measurement model. Identify how indications depend on the measurand and on influence
quantities such as environment, sample preparation, rater, device, software version, interference,
or proxy behavior. Choose sampling and instrument based on this model, not convenience alone.
Standardize the procedure, calibrate or anchor the indication, validate range and selectivity, and
retain provenance through data reduction.

Construct uncertainty from all material sources: sampling, repeatability, calibration references,
resolution, drift, environmental correction, rater or mode, model form, imputation, and transfer.
Avoid double counting, but do not omit systematic contributions because they do not appear in
replicate spread. During use, controls and drift signals decide whether a result can be released.

Report the result as a qualified package. State the value, unit or scale, uncertainty and basis,
method/version, time and context, traceability, detection or censoring state, quality flags, and
validity boundary. If the chain cannot support the use, the correct output may be “inconclusive,” a
repeat, a narrower claim, or a refusal to measure.

### Decision Rules

1. Define the measurand and decision use before choosing an instrument or collecting data.
2. State whether the indication is direct, proxy-based, self-reported, rated, sampled, destructive, or computationally inferred.
3. Record unit, scale, origin, transformation, frame, and population needed for interpretation.
4. Match instrument range, resolution, selectivity, burden, and disturbance to the intended use.
5. Design sampling to represent the target variation; do not treat instrument precision as sampling validity.
6. Calibrate and validate in the actual configuration, range, environment, mode, and population of use.
7. Preserve raw indication and every correction, reduction, exclusion, imputation, score, and rounding step.
8. Combine relevant uncertainty contributions and report them with an interpretable coverage or confidence basis.
9. Keep zero distinct from missing, invalid, censored, saturated, and below-detection states.
10. Stop or qualify reporting when quality controls, drift, calibration, reproducibility, or validity criteria fail.
11. Require equivalence evidence before comparing methods, sites, modes, versions, units, or frames.
12. Refuse uses outside validated boundaries or with consequences disproportionate to the evidence.

prose_key_components:
### Measurand and Attribute Specification (`measurand_and_attribute_specification`)

{"role_in_archetypes":"Anchors every unit, procedure, validity test, uncertainty claim, and decision to the same measurand.","typical_position":"input","required_properties":["target identity","attribute definition","condition and time","system boundary","population or instance scope"],"variation_dimensions":["physical quantity","analyte","latent construct","behavior","operational metric","derived index"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"measurand_and_attribute_specification","name":"Measurand and Attribute Specification","aliases":["measurement_target_specification"]},"classification":{"component_type":"object_or_artifact","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Defines the target, attribute or construct, system boundary, condition, time, and population being measured.","expanded_definition":"Gives the result one stable referent and distinguishes the intended attribute from convenient proxies, nearby constructs, and changing contextual realizations.","what_it_is_not":["It is not the instrument reading.","It is not a vague outcome label."]},"structural_role":{"role_in_archetypes":"Anchors every unit, procedure, validity test, uncertainty claim, and decision to the same measurand.","typical_position":"input","required_properties":["target identity","attribute definition","condition and time","system boundary","population or instance scope"],"variation_dimensions":["physical quantity","analyte","latent construct","behavior","operational metric","derived index"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required semantic anchor"}],"compatible_components":["intended_use_and_decision_link","operational_definition","measurement_model"],"incompatible_or_confusing_components":["instrument_output_label","proxy_without_target"],"selection_notes":"Reconcile with Measurement Construct Specification before index insertion.","validation_questions":["Could independent reviewers identify the same attribute?","Are condition and time part of the definition?","Is a proxy labeled separately?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["operational_definition","measurement_model"],"related_mechanisms":["measurement_protocol","measurement_system_validation_study"]},"examples":{"examples":[{"domain":"laboratory","example":"Mass concentration of a named analyte in a defined specimen matrix at preparation conditions."},{"domain":"software","example":"End-to-end request latency for an explicitly defined request population and event pair."}],"non_examples":["Quality","performance","or wellbeing without an attribute boundary."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","construct_definition_review"],"notes":"No frozen catalog mutation performed."}}

### Intended Use and Decision Link (`intended_use_and_decision_link`)

{"role_in_archetypes":"Sets acceptance criteria and determines how strong the measurement evidence must be.","typical_position":"criterion","required_properties":["named user","decision or claim","consequence","required discrimination","misuse boundary"],"variation_dimensions":["exploration","monitoring","screening","diagnosis","certification","enforcement","control"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"intended_use_and_decision_link","name":"Intended Use and Decision Link","aliases":["measurement_fitness_for_purpose_statement"]},"classification":{"component_type":"criterion","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"States why the result is needed, who will use it, the consequence, and the discrimination required.","expanded_definition":"Converts generic accuracy ambitions into fitness-for-purpose criteria for uncertainty, validity, timeliness, burden, range, and decision risk.","what_it_is_not":["It is not a post-hoc justification.","It is not the decision threshold alone."]},"structural_role":{"role_in_archetypes":"Sets acceptance criteria and determines how strong the measurement evidence must be.","typical_position":"criterion","required_properties":["named user","decision or claim","consequence","required discrimination","misuse boundary"],"variation_dimensions":["exploration","monitoring","screening","diagnosis","certification","enforcement","control"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required fitness criterion"}],"compatible_components":["measurand_and_attribute_specification","result_and_uncertainty_report","uncertainty_budget"],"incompatible_or_confusing_components":["instrument_convenience_rationale"],"selection_notes":"Define before method selection and revisit when decision use expands.","validation_questions":["What decision changes because of the result?","What error is consequential?","Is the method adequate for that use rather than merely available?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["measurand_and_attribute_specification"],"related_mechanisms":["measurement_system_validation_study"]},"examples":{"examples":[{"domain":"clinical","example":"Screening requires sensitivity and follow-up; diagnosis requires stronger specificity and validation."}],"non_examples":["Measure it because the dashboard has a field."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","high_stakes_use_review"],"notes":"Review consequence and proportionality during integration."}}

### Measurement Model (`measurement_model`)

{"role_in_archetypes":"Provides the causal or representational bridge from attribute to observed value and uncertainty sources.","typical_position":"transformation","required_properties":["inputs and outputs","influence quantities","proxy assumptions","corrections","valid range"],"variation_dimensions":["linear or nonlinear","deterministic or probabilistic","direct or proxy","static or dynamic"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"measurement_model","name":"Measurement Model","aliases":["observation_to_measurand_model"]},"classification":{"component_type":"transformation","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Relates the measurand to indications, observations, ratings, influence quantities, corrections, and outputs.","expanded_definition":"Makes explicit how instrument response or human/computational observation depends on the target and nuisance conditions, including proxies and transformations.","what_it_is_not":["It is not a calibration curve alone.","It is not the final statistical analysis model."]},"structural_role":{"role_in_archetypes":"Provides the causal or representational bridge from attribute to observed value and uncertainty sources.","typical_position":"transformation","required_properties":["inputs and outputs","influence quantities","proxy assumptions","corrections","valid range"],"variation_dimensions":["linear or nonlinear","deterministic or probabilistic","direct or proxy","static or dynamic"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required observation bridge"}],"compatible_components":["instrument_and_sensor_profile","calibration_and_traceability_chain","uncertainty_budget"],"incompatible_or_confusing_components":["unexplained_score_formula"],"selection_notes":"Include variables whose omission would change interpretation or uncertainty.","validation_questions":["What produces the indication besides the target?","Which corrections are applied?","Where does the model fail?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["data_reduction_and_scoring_rule"],"related_mechanisms":["measurement_system_validation_study","uncertainty_budget_table"]},"examples":{"examples":[{"domain":"environmental","example":"Sensor response modeled from concentration with temperature"}],"non_examples":["The device gives the true value."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","model_validity_review"],"notes":"Reconcile with Transformation Rule and signal-semantics components."}}

### Unit, Scale, and Reference Frame (`unit_scale_and_reference_frame`)

{"role_in_archetypes":"Establishes the semantics of the reported value and permissible comparisons.","typical_position":"boundary","required_properties":["unit or category semantics","scale type","origin","frame","allowed transformations"],"variation_dimensions":["nominal","ordinal","interval","ratio","probability","spatial frame","population norm"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"unit_scale_and_reference_frame","name":"Unit, Scale, and Reference Frame","aliases":["measurement_value_frame"]},"classification":{"component_type":"boundary","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Defines value representation, ordering, intervals, origin, transformation rules, reference population, and comparison frame.","expanded_definition":"Keeps physical units, ordinal and interval scales, categories, scores, indices, coordinates, and baselines interpretable and comparable only under justified transformations.","what_it_is_not":["It is not a display format.","It is not a label copied from a source system."]},"structural_role":{"role_in_archetypes":"Establishes the semantics of the reported value and permissible comparisons.","typical_position":"boundary","required_properties":["unit or category semantics","scale type","origin","frame","allowed transformations"],"variation_dimensions":["nominal","ordinal","interval","ratio","probability","spatial frame","population norm"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required value semantics"}],"compatible_components":["measurement_model","interoperability_and_harmonization_crosswalk"],"incompatible_or_confusing_components":["unitless_number_without_scale"],"selection_notes":"State conversion and comparison restrictions explicitly.","validation_questions":["What does a difference of one mean?","Is zero meaningful?","Which frame or reference population applies?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["reference_material_or_standard"],"related_mechanisms":["calibration_traceability_record"]},"examples":{"examples":[{"domain":"physics","example":"Temperature in a named unit under a defined reference condition."},{"domain":"survey","example":"A bounded ordinal response scale with population and version."}],"non_examples":["A score of 73 with no scale definition."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","scale_semantics_review"],"notes":"Check for existing scale and frame components."}}

### Operational Definition (`operational_definition`)

{"role_in_archetypes":"Bridges conceptual meaning and repeatable observation.","typical_position":"criterion","required_properties":["observable criteria","inclusion and exclusion","context","version","ambiguity handling"],"variation_dimensions":["physical protocol","diagnostic criteria","rubric","survey item set","event definition"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"operational_definition","name":"Operational Definition","aliases":["measurement_construct_operationalization"]},"classification":{"component_type":"criterion","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Converts an attribute or construct into observable inclusion, exclusion, condition, coding, and scoring criteria.","expanded_definition":"Makes abstract labels realizable without claiming that one operationalization exhausts the construct across every culture, population, or context.","what_it_is_not":["It is not the construct itself.","It is not an unchangeable universal definition."]},"structural_role":{"role_in_archetypes":"Bridges conceptual meaning and repeatable observation.","typical_position":"criterion","required_properties":["observable criteria","inclusion and exclusion","context","version","ambiguity handling"],"variation_dimensions":["physical protocol","diagnostic criteria","rubric","survey item set","event definition"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required realization criterion"}],"compatible_components":["measurand_and_attribute_specification","measurement_procedure","validity_and_selectivity_evidence"],"incompatible_or_confusing_components":["construct_name_only"],"selection_notes":"Validate meaning with domain experts and affected populations where relevant.","validation_questions":["Can observers apply it consistently?","What relevant meaning is excluded?","Does it vary across groups or contexts?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["measurand_and_attribute_specification"],"related_mechanisms":["measurement_protocol","blinded_rater_assessment"]},"examples":{"examples":[{"domain":"software","example":"A request-latency event pair and inclusion rule."},{"domain":"health","example":"Symptom criteria and recall period for a scale."}],"non_examples":["Everyone knows what quality means."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","construct_validity_review"],"notes":"Reconcile with construct-specification records."}}

### Instrument and Sensor Profile (`instrument_and_sensor_profile`)

{"role_in_archetypes":"Selects and characterizes the observation channel that realizes the measurement model.","typical_position":"resource","required_properties":["identity and version","range","resolution","response","selectivity","environment","maintenance"],"variation_dimensions":["device","assay","rater","questionnaire","algorithm","sensor network"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"instrument_and_sensor_profile","name":"Instrument and Sensor Profile","aliases":["measurement_instrument_profile"]},"classification":{"component_type":"resource","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Records device, assay, rater, rubric, questionnaire, estimator, range, resolution, selectivity, and configuration.","expanded_definition":"Treats human and computational observation systems as instruments with identifiable performance, versions, dependencies, and failure boundaries.","what_it_is_not":["It is not a product datasheet alone.","It is not evidence of fitness for purpose by itself."]},"structural_role":{"role_in_archetypes":"Selects and characterizes the observation channel that realizes the measurement model.","typical_position":"resource","required_properties":["identity and version","range","resolution","response","selectivity","environment","maintenance"],"variation_dimensions":["device","assay","rater","questionnaire","algorithm","sensor network"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required realization resource"}],"compatible_components":["measurement_model","calibration_and_traceability_chain","quality_control_and_drift_monitor"],"incompatible_or_confusing_components":["instrument_name_without_configuration"],"selection_notes":"Match performance to the intended use and actual environment.","validation_questions":["Does range cover use?","Which influences alter response?","Is the exact version traceable?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["measurement_procedure"],"related_mechanisms":["reference_material_comparison","instrument_drift_control_chart"]},"examples":{"examples":[{"domain":"laboratory","example":"Assay lot"}],"non_examples":["Measured with Sensor X."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","instrument_performance_review"],"notes":"Check overlap with Standardized Instrument Set."}}

### Sampling and Observation Design (`sampling_and_observation_design`)

{"role_in_archetypes":"Determines which variation the result represents and which inference boundary is justified.","typical_position":"process","required_properties":["target population","sampling frame","selection rule","time and space","replicates","nonresponse"],"variation_dimensions":["random","stratified","purposive","temporal","spatial","event-triggered","destructive"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"sampling_and_observation_design","name":"Sampling and Observation Design","aliases":["measurement_sampling_frame"]},"classification":{"component_type":"pathway","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Selects cases, units, specimens, locations, times, modes, replicates, and aggregation windows.","expanded_definition":"Connects observed instances to the target population or process and separates sampling uncertainty from instrument and model uncertainty.","what_it_is_not":["It is not the instrument sampling rate alone.","It is not a convenience sample left implicit."]},"structural_role":{"role_in_archetypes":"Determines which variation the result represents and which inference boundary is justified.","typical_position":"process","required_properties":["target population","sampling frame","selection rule","time and space","replicates","nonresponse"],"variation_dimensions":["random","stratified","purposive","temporal","spatial","event-triggered","destructive"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required representation pathway"}],"compatible_components":["measurement_procedure","uncertainty_budget","repeatability_reproducibility_profile"],"incompatible_or_confusing_components":["instrument_resolution"],"selection_notes":"Design around target variation and decision timing, not only collection convenience.","validation_questions":["What population or interval is represented?","Which cases cannot be observed?","Are replicates independent and meaningful?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["missing_censoring_and_detection_limit_policy"],"related_mechanisms":["measurement_protocol","interlaboratory_comparison"]},"examples":{"examples":[{"domain":"environment","example":"Stratified spatial sensor placement with duplicate reference sites and seasonal coverage."}],"non_examples":["Use whatever records are already complete."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","sampling_review"],"notes":"Reconcile with sampling and timing components."}}

### Calibration and Traceability Chain (`calibration_and_traceability_chain`)

{"role_in_archetypes":"Grounds value assignment and comparability in auditable reference evidence.","typical_position":"support","required_properties":["reference identity","comparison result","unbroken link","uncertainty","range and condition","date and configuration"],"variation_dimensions":["formal metrological","reference material","transfer standard","consensus reference","operational baseline"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"calibration_and_traceability_chain","name":"Calibration and Traceability Chain","aliases":["measurement_reference_chain"]},"classification":{"component_type":"pathway","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Links indications to references through documented comparisons and uncertainty contributions.","expanded_definition":"Establishes whether a result is traceable to a unit realization, reference material, consensus value, baseline, or other governed reference in the actual use envelope.","what_it_is_not":["It is not a certificate filename.","It is not calibration at one point used for every range."]},"structural_role":{"role_in_archetypes":"Grounds value assignment and comparability in auditable reference evidence.","typical_position":"support","required_properties":["reference identity","comparison result","unbroken link","uncertainty","range and condition","date and configuration"],"variation_dimensions":["formal metrological","reference material","transfer standard","consensus reference","operational baseline"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required reference pathway when applicable"}],"compatible_components":["reference_material_or_standard","uncertainty_budget","instrument_and_sensor_profile"],"incompatible_or_confusing_components":["expired_unmatched_calibration_certificate"],"selection_notes":"State limitations honestly where formal traceability is unavailable.","validation_questions":["Does evidence cover the actual configuration and range?","Are uncertainty contributions propagated?","Is every link identifiable?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["reference_material_or_standard"],"related_mechanisms":["calibration_traceability_record","reference_material_comparison"]},"examples":{"examples":[{"domain":"metrology","example":"Instrument comparison through transfer standards to a unit realization with uncertainty at each link."}],"non_examples":["Calibrated sometime last year."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","metrology_review"],"notes":"Reconcile against calibration and traceability components."}}

### Measurement Procedure (`measurement_procedure`)

{"role_in_archetypes":"Governs execution from target preparation through raw indication capture.","typical_position":"process","required_properties":["version","steps","conditions","settings","timing","deviations","stop rules"],"variation_dimensions":["laboratory SOP","interview administration","automated capture","field protocol","coding workflow"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"measurement_procedure","name":"Measurement Procedure","aliases":["measurement_method_procedure"]},"classification":{"component_type":"pathway","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Defines preparation, administration, environment, sequence, timing, capture, cleaning, and deviation handling.","expanded_definition":"Makes realization repeatable enough for comparison while preserving contextual conditions and justified departures from the standard method.","what_it_is_not":["It is not an instrument manual.","It is not a result-report template."]},"structural_role":{"role_in_archetypes":"Governs execution from target preparation through raw indication capture.","typical_position":"process","required_properties":["version","steps","conditions","settings","timing","deviations","stop rules"],"variation_dimensions":["laboratory SOP","interview administration","automated capture","field protocol","coding workflow"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required execution pathway"}],"compatible_components":["instrument_and_sensor_profile","sampling_and_observation_design","quality_control_and_drift_monitor"],"incompatible_or_confusing_components":["undocumented_operator_customization"],"selection_notes":"Reuse existing measurement-protocol components where semantically equivalent.","validation_questions":["Can another trained operator reproduce conditions?","Are deviations recorded?","Do stop rules protect validity?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["operational_definition"],"related_mechanisms":["measurement_protocol","blinded_rater_assessment"]},"examples":{"examples":[{"domain":"survey","example":"Script"}],"non_examples":["Ask the questions consistently."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","protocol_reuse_review"],"notes":"Likely reuse/merge candidate with standardization components."}}

### Uncertainty Budget (`uncertainty_budget`)

{"role_in_archetypes":"Converts imperfect evidence into an honest interval, distribution, grade, or decision limitation.","typical_position":"measurement","required_properties":["source inventory","magnitude and basis","combination rule","correlations","coverage interpretation"],"variation_dimensions":["analytic","simulation","empirical","interval","posterior","qualitative grade"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"uncertainty_budget","name":"Uncertainty Budget","aliases":["measurement_uncertainty_budget"]},"classification":{"component_type":"object_or_artifact","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Identifies, quantifies, combines, and reports relevant random and systematic uncertainty contributions.","expanded_definition":"Includes sampling, repeatability, calibration, resolution, environment, model, rater, correction, missingness, and transfer terms where material, with correlations and coverage basis.","what_it_is_not":["It is not tolerance.","It is not display resolution.","It is not repeatability alone."]},"structural_role":{"role_in_archetypes":"Converts imperfect evidence into an honest interval, distribution, grade, or decision limitation.","typical_position":"measurement","required_properties":["source inventory","magnitude and basis","combination rule","correlations","coverage interpretation"],"variation_dimensions":["analytic","simulation","empirical","interval","posterior","qualitative grade"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required confidence object"}],"compatible_components":["measurement_model","calibration_and_traceability_chain","repeatability_reproducibility_profile"],"incompatible_or_confusing_components":["single_standard_deviation_as_total_uncertainty"],"selection_notes":"Tailor expression to domain while retaining all material contributors.","validation_questions":["Which systematic sources are absent from repeats?","Are terms double counted?","What does the interval or grade mean?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["result_and_uncertainty_report"],"related_mechanisms":["uncertainty_budget_table"]},"examples":{"examples":[{"domain":"laboratory","example":"Combined calibration"}],"non_examples":["The instrument has three decimal places."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","uncertainty_specialist_review"],"notes":"Reconcile with existing uncertainty components."}}

### Data Reduction and Scoring Rule (`data_reduction_and_scoring_rule`)

{"role_in_archetypes":"Preserves the auditable mapping from raw observation to reported value.","typical_position":"transformation","required_properties":["versioned formula or workflow","raw-data links","missingness rules","rounding","validation"],"variation_dimensions":["formula","algorithm","rubric scoring","signal processing","statistical estimate"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"data_reduction_and_scoring_rule","name":"Data Reduction and Scoring Rule","aliases":["measurement_result_transformation_rule"]},"classification":{"component_type":"transformation","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Transforms raw indications into corrected, aggregated, coded, scored, and rounded results with provenance.","expanded_definition":"Governs filtering, baseline subtraction, calibration application, interpolation, imputation, aggregation, normalization, scoring, and significant digits.","what_it_is_not":["It is not an opaque spreadsheet formula.","It is not the downstream decision rule."]},"structural_role":{"role_in_archetypes":"Preserves the auditable mapping from raw observation to reported value.","typical_position":"transformation","required_properties":["versioned formula or workflow","raw-data links","missingness rules","rounding","validation"],"variation_dimensions":["formula","algorithm","rubric scoring","signal processing","statistical estimate"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required result transformation"}],"compatible_components":["measurement_model","missing_censoring_and_detection_limit_policy","result_and_uncertainty_report"],"incompatible_or_confusing_components":["manual_unlogged_adjustment"],"selection_notes":"Preserve intermediate states when they affect audit or uncertainty.","validation_questions":["Can the result be reproduced from raw data?","Are exclusions and corrections visible?","Does transformation preserve scale meaning?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["measurement_model"],"related_mechanisms":["measurement_protocol"]},"examples":{"examples":[{"domain":"software","example":"Clock correction"}],"non_examples":["Clean the data and compute the score."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","transformation_provenance_review"],"notes":"Reconcile with Transformation Rule component."}}

### Validity and Selectivity Evidence (`validity_and_selectivity_evidence`)

{"role_in_archetypes":"Determines whether the value means what users claim it means.","typical_position":"criterion","required_properties":["claim-specific evidence","rival explanation tests","range and population","subgroup review","revision trigger"],"variation_dimensions":["physical selectivity","construct validity","criterion validity","proxy fidelity","diagnostic performance"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"validity_and_selectivity_evidence","name":"Validity and Selectivity Evidence","aliases":["measurement_validity_evidence"]},"classification":{"component_type":"criterion","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Shows that the method responds to the intended attribute rather than nuisance influences or an unstable proxy.","expanded_definition":"Integrates content, construct, criterion, selectivity, interference, sensitivity, transport, subgroup, and use-boundary evidence as appropriate to domain.","what_it_is_not":["It is not reliability alone.","It is not correlation with one convenient criterion."]},"structural_role":{"role_in_archetypes":"Determines whether the value means what users claim it means.","typical_position":"criterion","required_properties":["claim-specific evidence","rival explanation tests","range and population","subgroup review","revision trigger"],"variation_dimensions":["physical selectivity","construct validity","criterion validity","proxy fidelity","diagnostic performance"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required meaning evidence"}],"compatible_components":["operational_definition","measurement_model","intended_use_and_decision_link"],"incompatible_or_confusing_components":["high_repeatability_as_validity"],"selection_notes":"Evidence must match the intended interpretation and consequence.","validation_questions":["Could another attribute explain the indication?","Does validity transfer across groups and modes?","Is the proxy relationship maintained?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["repeatability_reproducibility_profile"],"related_mechanisms":["measurement_system_validation_study","reference_material_comparison"]},"examples":{"examples":[{"domain":"assay","example":"Interference"},{"domain":"survey","example":"Content"}],"non_examples":["The measure is valid because alpha is high."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","validity_specialist_review"],"notes":"High-priority specialist review."}}

### Repeatability and Reproducibility Profile (`repeatability_reproducibility_profile`)

{"role_in_archetypes":"Tests whether claimed precision and comparability survive repeated realization.","typical_position":"measurement","required_properties":["repeat conditions","changed factors","agreement metric","range","uncertainty","acceptance criterion"],"variation_dimensions":["within-run","between-run","rater","instrument","site","mode","temporal"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"repeatability_reproducibility_profile","name":"Repeatability and Reproducibility Profile","aliases":["measurement_precision_profile"]},"classification":{"component_type":"metric","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Quantifies agreement under same-condition repeats and changed operators, instruments, sites, modes, or times.","expanded_definition":"Separates within-run variation from realistic implementation variation and identifies interaction, heterogeneity, and stability limits.","what_it_is_not":["It is not validity.","It is not accuracy.","It is not one correlation coefficient."]},"structural_role":{"role_in_archetypes":"Tests whether claimed precision and comparability survive repeated realization.","typical_position":"measurement","required_properties":["repeat conditions","changed factors","agreement metric","range","uncertainty","acceptance criterion"],"variation_dimensions":["within-run","between-run","rater","instrument","site","mode","temporal"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required precision evidence"}],"compatible_components":["uncertainty_budget","quality_control_and_drift_monitor"],"incompatible_or_confusing_components":["correlation_without_agreement"],"selection_notes":"Select agreement statistics appropriate to scale and decision.","validation_questions":["Which factors were varied?","Does study cover the use range?","Are disagreements consequential?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["validity_and_selectivity_evidence"],"related_mechanisms":["gauge_repeatability_reproducibility_study","interlaboratory_comparison"]},"examples":{"examples":[{"domain":"manufacturing","example":"Operator-by-part gauge variation across the working range."}],"non_examples":["Two raters correlate because both rank subjects similarly but differ systematically in level."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","agreement_metric_review"],"notes":"Reconcile with reproducibility components."}}

### Quality Control and Drift Monitor (`quality_control_and_drift_monitor`)

{"role_in_archetypes":"Maintains the validated measurement state during operation.","typical_position":"measurement","required_properties":["control material or signal","baseline","limits","cadence","decision rule","investigation record"],"variation_dimensions":["run controls","control charts","duplicate samples","drift alarms","rater calibration"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"quality_control_and_drift_monitor","name":"Quality Control and Drift Monitor","aliases":["measurement_performance_monitor"]},"classification":{"component_type":"feedback_signal","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Uses references, blanks, controls, duplicates, charts, and deviations to detect invalid runs or changing performance.","expanded_definition":"Converts ongoing evidence into hold, repeat, investigate, recalibrate, release, and retirement actions before bad results accumulate.","what_it_is_not":["It is not a dashboard without action rules.","It is not periodic calibration alone."]},"structural_role":{"role_in_archetypes":"Maintains the validated measurement state during operation.","typical_position":"measurement","required_properties":["control material or signal","baseline","limits","cadence","decision rule","investigation record"],"variation_dimensions":["run controls","control charts","duplicate samples","drift alarms","rater calibration"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required operational guardrail"}],"compatible_components":["instrument_and_sensor_profile","measurement_stewardship_and_versioning"],"incompatible_or_confusing_components":["alert_without_release_control"],"selection_notes":"Match controls to likely failure modes and decision consequences.","validation_questions":["Can control failure block release?","Are limits evidence-based?","Does investigation preserve provenance?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["repeatability_reproducibility_profile"],"related_mechanisms":["instrument_drift_control_chart","reference_material_comparison"]},"examples":{"examples":[{"domain":"laboratory","example":"Blank"}],"non_examples":["Review averages monthly after all results were released."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","quality_control_review"],"notes":"Reconcile with Calibration and Drift Check."}}

### Result and Uncertainty Report (`result_and_uncertainty_report`)

{"role_in_archetypes":"Carries the completed measurement chain into comparison and decision contexts.","typical_position":"output","required_properties":["value or status","unit and scale","uncertainty","method/version","time/context","flags","boundary"],"variation_dimensions":["scalar","interval","distribution","category","profile","map","time series"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"result_and_uncertainty_report","name":"Result and Uncertainty Report","aliases":["qualified_measurement_result"]},"classification":{"component_type":"object_or_artifact","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Keeps value, unit or scale, uncertainty, method, time, context, traceability, flags, and validity boundary together.","expanded_definition":"Defines the transferable evidence object so downstream systems cannot strip away the qualifications needed for interpretation and safe use.","what_it_is_not":["It is not a bare numeric field.","It is not a certainty statement."]},"structural_role":{"role_in_archetypes":"Carries the completed measurement chain into comparison and decision contexts.","typical_position":"output","required_properties":["value or status","unit and scale","uncertainty","method/version","time/context","flags","boundary"],"variation_dimensions":["scalar","interval","distribution","category","profile","map","time series"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required qualified output"}],"compatible_components":["uncertainty_budget","unit_scale_and_reference_frame","measurement_stewardship_and_versioning"],"incompatible_or_confusing_components":["unqualified_dashboard_number"],"selection_notes":"Design schema so value cannot travel without critical metadata.","validation_questions":["Can a downstream user recover meaning?","Are invalid or inconclusive states represented?","Does uncertainty remain attached?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["uncertainty_budget"],"related_mechanisms":["calibration_traceability_record"]},"examples":{"examples":[{"domain":"laboratory","example":"Concentration"}],"non_examples":[12.47]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","reporting_schema_review"],"notes":"Reconcile with uncertainty-reporting components."}}

### Measurement Stewardship and Versioning (`measurement_stewardship_and_versioning`)

{"role_in_archetypes":"Maintains one governed measurement identity over its lifecycle.","typical_position":"support","required_properties":["named steward","version registry","change review","overlap or crosswalk rule","training","retirement"],"variation_dimensions":["laboratory manager","data product owner","clinical governance","standards body","instrument custodian"]}

Semantic canonical mapping retained the complete legacy component record: {"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"measurement_stewardship_and_versioning","name":"Measurement Stewardship and Versioning","aliases":["measurement_method_governance"]},"classification":{"component_type":"actor_or_role","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Assigns ownership for method approval, change control, training, records, equivalence, and retirement.","expanded_definition":"Preserves historical interpretability and accountability when instruments, software, references, procedures, scoring, populations, or uses change.","what_it_is_not":["It is not generic project ownership.","It is not permission for silent method edits."]},"structural_role":{"role_in_archetypes":"Maintains one governed measurement identity over its lifecycle.","typical_position":"support","required_properties":["named steward","version registry","change review","overlap or crosswalk rule","training","retirement"],"variation_dimensions":["laboratory manager","data product owner","clinical governance","standards body","instrument custodian"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"required lifecycle authority"}],"compatible_components":["quality_control_and_drift_monitor","interoperability_and_harmonization_crosswalk"],"incompatible_or_confusing_components":["unreviewed_local_method_fork"],"selection_notes":"Separate technical custody, approval authority, and ethical/data governance where needed.","validation_questions":["Who can change the method?","How are historical values affected?","What triggers retirement or revalidation?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["interoperability_and_harmonization_crosswalk"],"related_mechanisms":["measurement_protocol","interlaboratory_comparison"]},"examples":{"examples":[{"domain":"public_health","example":"A governed registry records instrument"}],"non_examples":["The spreadsheet owner can update formulas at any time."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","governance_review"],"notes":"Reconcile with stewardship/version components."}} Consolidated omitted legacy component records: [{"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"reference_material_or_standard","name":"Reference Material or Standard","aliases":["measurement_reference_artifact"]},"classification":{"component_type":"resource","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Provides a stable artifact, signal, definition, population, or consensus value for calibration or validation.","expanded_definition":"Supplies an assigned property and uncertainty, provenance, commutability or applicability, storage and handling requirements, and validity period.","what_it_is_not":["It is not automatically truth.","It is not interchangeable across matrices or populations without evidence."]},"structural_role":{"role_in_archetypes":"Conditional anchor for value assignment, bias assessment, and comparability.","typical_position":"resource","required_properties":["identity","assigned value or definition","uncertainty","applicability","stability","custody"],"variation_dimensions":["physical standard","reference material","benchmark dataset","anchor cases","population norm"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"optional reference anchor"}],"compatible_components":["calibration_and_traceability_chain","quality_control_and_drift_monitor"],"incompatible_or_confusing_components":["unverified_convenience_sample"],"selection_notes":"Use only when reference behavior is fit for the measurand and method.","validation_questions":["Is the reference commutable or applicable?","Is assigned uncertainty known?","Is custody and stability controlled?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["calibration_and_traceability_chain"],"related_mechanisms":["reference_material_comparison"]},"examples":{"examples":[{"domain":"chemistry","example":"Certified reference material with matrix"}],"non_examples":["One old sample assumed to be correct."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","reference_standard_review"],"notes":"Optional depending on domain."}},{"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"rater_training_and_blinding","name":"Rater Training and Blinding","aliases":["observer_calibration_and_masking"]},"classification":{"component_type":"condition","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Controls human interpretation, expectancy, drift, and differential administration in observation and scoring.","expanded_definition":"Defines competency, anchors, practice, masking, duplicate rating, feedback, retraining, and adjudication while preserving independent disagreement evidence.","what_it_is_not":["It is not forced consensus among raters.","It is not a substitute for a valid rubric."]},"structural_role":{"role_in_archetypes":"Conditional human-instrument performance control.","typical_position":"support","required_properties":["training standard","competency check","blinding scope","drift monitoring","disagreement handling"],"variation_dimensions":["clinical rating","behavioral coding","image interpretation","qualitative coding"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"optional human-rater control"}],"compatible_components":["operational_definition","repeatability_reproducibility_profile"],"incompatible_or_confusing_components":["post_rating_group_conformity"],"selection_notes":"Required when human judgment creates or materially transforms indications.","validation_questions":["What information could bias rating?","Is competence checked?","Is independent disagreement retained?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["measurement_procedure"],"related_mechanisms":["blinded_rater_assessment","gauge_repeatability_reproducibility_study"]},"examples":{"examples":[{"domain":"imaging","example":"Blinded readers train on anchors and undergo duplicate agreement checks."}],"non_examples":["Raters discuss every case until they agree before reliability is assessed."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","bias_review"],"notes":"Reconcile with Rater Training and Masking."}},{"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"disturbance_and_reactivity_budget","name":"Disturbance and Reactivity Budget","aliases":["measurement_back_action_budget"]},"classification":{"component_type":"constraint","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Assesses how observation changes the target, specimen, participant, context, or system.","expanded_definition":"Identifies loading, consumption, heating, contamination, expectancy, behavior change, traffic overhead, feedback, and privacy effects and bounds acceptable disturbance.","what_it_is_not":["It is not uncertainty alone.","It is not an excuse to ignore observer effects."]},"structural_role":{"role_in_archetypes":"Conditional guardrail when obtaining information perturbs what is measured.","typical_position":"constraint","required_properties":["disturbance pathway","magnitude","consequence","limit","mitigation","residual effect"],"variation_dimensions":["physical loading","destructive sampling","behavioral reactivity","system overhead"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"optional perturbation guardrail"}],"compatible_components":["measurement_model","intended_use_and_decision_link"],"incompatible_or_confusing_components":["observer_effect_ignored"],"selection_notes":"Becomes required whenever disturbance could change value or harm the target.","validation_questions":["What changes because measurement occurs?","Is burden proportional?","Can a safer proxy or nonintrusive method work?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["measurement_model"],"related_mechanisms":["measurement_system_validation_study"]},"examples":{"examples":[{"domain":"software","example":"Tracing overhead measured against the latency it seeks to characterize."},{"domain":"biology","example":"Specimen consumption and preparation effects budgeted."}],"non_examples":["Assume observation is passive."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","disturbance_ethics_review"],"notes":"Strong neighbor to q9 target but no q9 disposition made."}},{"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"missing_censoring_and_detection_limit_policy","name":"Missing, Censoring, and Detection-Limit Policy","aliases":["measurement_nonordinary_result_policy"]},"classification":{"component_type":"decision_rule","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Distinguishes unavailable, invalid, saturated, censored, below-detection, and true-zero states.","expanded_definition":"Defines capture, coding, estimation, uncertainty, reporting, and decision behavior where the measurement system cannot produce an ordinary value.","what_it_is_not":["It is not zero imputation.","It is not deletion of inconvenient observations."]},"structural_role":{"role_in_archetypes":"Conditional guardrail against semantic corruption at observation boundaries.","typical_position":"decision","required_properties":["state taxonomy","detection and quantification limits","coding","analysis rule","report language"],"variation_dimensions":["left or right censoring","saturation","intermittent missingness","invalid run","nonresponse"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"optional boundary policy"}],"compatible_components":["data_reduction_and_scoring_rule","result_and_uncertainty_report"],"incompatible_or_confusing_components":["missing_equals_zero"],"selection_notes":"Required whenever ordinary values are unavailable over part of the domain.","validation_questions":["Can states be distinguished at capture?","Is estimation justified?","Does reporting preserve the boundary?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["sampling_and_observation_design"],"related_mechanisms":["limit_of_detection_estimation"]},"examples":{"examples":[{"domain":"assay","example":"Below detection"}],"non_examples":["Replace every nonnumeric result with zero."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","missingness_review"],"notes":"Reconcile with detection-limit and censoring components."}},{"metadata":{"schema_version":"1.0","artifact_type":"solution_component","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"interoperability_and_harmonization_crosswalk","name":"Interoperability and Harmonization Crosswalk","aliases":["measurement_method_crosswalk"]},"classification":{"component_type":"transformation","abstraction_level":"reusable_midlevel","maturity":"provisional"},"definition":{"short_definition":"Relates methods, units, modes, sites, and versions without assuming equivalence.","expanded_definition":"Records conversion rules, overlap evidence, residual bias, uncertainty, applicability, breakpoints, and exceptions needed to compare or migrate measurement series.","what_it_is_not":["It is not a string-based field mapping.","It is not proof that different constructs are equal."]},"structural_role":{"role_in_archetypes":"Conditional bridge preserving comparability when one common method cannot be used.","typical_position":"transformation","required_properties":["source and target method","overlap evidence","conversion","uncertainty","boundary","version"],"variation_dimensions":["unit conversion","instrument method","survey mode","site harmonization","historical version"]},"use":{"used_by_archetypes":[{"slug":"traceable_measurement_system_design","role":"optional transfer bridge"}],"compatible_components":["unit_scale_and_reference_frame","measurement_stewardship_and_versioning"],"incompatible_or_confusing_components":["assumed_equivalence"],"selection_notes":"Prefer overlap data and uncertainty-aware conversion; annotate discontinuities when equivalence fails.","validation_questions":["Are constructs and frames compatible?","What residual bias remains?","Where is direct comparison invalid?"]},"relationships":{"parent_component":"","child_components":[],"related_components":["measurement_stewardship_and_versioning"],"related_mechanisms":["interlaboratory_comparison"]},"examples":{"examples":[{"domain":"health","example":"Overlap study linking old and new assay methods with bias"}],"non_examples":["Rename both fields the same and concatenate the series."]},"review":{"review_status":"not_reviewed","review_flags":["component_extraction_review","harmonization_review"],"notes":"Reconcile with mapping-reconciliation components."}}]

prose_common_mechanisms:
### Measurement Protocol (`measurement_protocol`)

**Type:** `document`

{"short_definition":"Specifies preparation, administration, settings, sampling, capture, reduction, quality control, deviations, and reporting for one measurement method.","expanded_definition":"Turns the approved measurement model into an executable and versioned procedure, retaining conditions and exceptions necessary for repeatability and audit.","mechanism_not_archetype_reason":"It is the procedural document implementing a selected measurement design, not the full measurand-to-qualified-result lifecycle."}

Selection constraints and retained implementation evidence: trained operator or validated automation; controlled version; available quality controls; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"measurement_protocol","name":"Measurement Protocol","aliases":["measurement_method_protocol"]},"classification":{"mechanism_type":"document","domain_specificity":"cross_domain_with_adaptation","maturity":"common"},"definition":{"short_definition":"Specifies preparation, administration, settings, sampling, capture, reduction, quality control, deviations, and reporting for one measurement method.","expanded_definition":"Turns the approved measurement model into an executable and versioned procedure, retaining conditions and exceptions necessary for repeatability and audit.","mechanism_not_archetype_reason":"It is the procedural document implementing a selected measurement design, not the full measurand-to-qualified-result lifecycle."},"implementation":{"operating_logic":"Bind a method version to target preparation, instrument configuration, observation sequence, capture, controls, transformation, release, and exception handling.","typical_steps":["identify measurand and method version","prepare target or specimen","verify environment and instrument state","execute sampling and observation sequence","capture raw indications","run controls","apply reduction rule","review deviations","authorize qualified result"],"required_inputs":["measurand specification","instrument profile","sampling plan","data-reduction rule","quality criteria"],"outputs_or_deliverables":["raw and processed records","deviation log","qualified result","execution trace"],"required_conditions":["trained operator or validated automation","controlled version","available quality controls"],"failure_conditions":["silent deviation","wrong method version","omitted raw data","uncontrolled environment","release despite failed controls"],"maintenance_or_update_needs":["change control","training","periodic review","equivalence evidence","retirement record"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"primary execution mechanism"}],"supports_components":["measurement_procedure","operational_definition","data_reduction_and_scoring_rule","quality_control_and_drift_monitor"],"can_be_confused_with_archetypes":["measurement_protocol_standardization"],"neighboring_mechanisms":["calibration_traceability_record","measurement_system_validation_study"]},"operational_profile":{"strengths":["repeatable execution","auditable deviations","version control","training basis"],"limitations":["does not establish validity by itself","can become ritual detached from actual practice"],"tradeoffs":["standardization versus contextual flexibility","detail versus usability"],"misuse_risks":["copying an SOP without validating local use","suppressing justified deviations","assuming documentation equals conformance"],"metrics_for_success":["protocol deviation rate","retraining events","reproducibility","invalid-run containment"],"ethical_or_safety_notes":["Procedures involving people or destructive sampling require proportionality","consent","and stop rules."]},"examples":{"examples":[{"domain":"laboratory","example":"A controlled assay protocol from specimen preparation through uncertainty-qualified release.","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["A device quick-start guide with no measurand","controls","or reporting rule."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","reconcile_with_measurement_SOP"],"notes":"Likely merge candidate with existing measurement standard operating procedure."}}

### Calibration Traceability Record (`calibration_traceability_record`)

**Type:** `document`

{"short_definition":"Preserves reference comparisons, certificates, configuration, range, conditions, dates, results, and uncertainty links.","expanded_definition":"Makes it possible to determine whether a reported result is anchored through an applicable and unbroken reference chain for the actual use envelope.","mechanism_not_archetype_reason":"It is a reference-evidence artifact inside measurement design and maintenance, not the complete intervention."}

Selection constraints and retained implementation evidence: reference fit; controlled configuration; competent comparison; retained provenance; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"calibration_traceability_record","name":"Calibration Traceability Record","aliases":["measurement_reference_chain_record"]},"classification":{"mechanism_type":"document","domain_specificity":"cross_domain_with_adaptation","maturity":"common"},"definition":{"short_definition":"Preserves reference comparisons, certificates, configuration, range, conditions, dates, results, and uncertainty links.","expanded_definition":"Makes it possible to determine whether a reported result is anchored through an applicable and unbroken reference chain for the actual use envelope.","mechanism_not_archetype_reason":"It is a reference-evidence artifact inside measurement design and maintenance, not the complete intervention."},"implementation":{"operating_logic":"Record each comparison from instrument indication to reference, including identity, method, result, uncertainty, range, conditions, and validity period.","typical_steps":["identify instrument and configuration","identify reference and assigned value","execute comparison across range","estimate correction and uncertainty","record traceability links","approve applicability period","attach record to method"],"required_inputs":["instrument profile","reference identity","comparison procedure","uncertainty evidence"],"outputs_or_deliverables":["calibration result","correction model","traceability chain","applicability statement"],"required_conditions":["reference fit","controlled configuration","competent comparison","retained provenance"],"failure_conditions":["wrong configuration","expired or noncommutable reference","range extrapolation","broken chain","missing uncertainty"],"maintenance_or_update_needs":["recalibration cadence","drift-triggered review","reference expiry","configuration-change review"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"traceability evidence mechanism"}],"supports_components":["calibration_and_traceability_chain","reference_material_or_standard","result_and_uncertainty_report"],"can_be_confused_with_archetypes":["non_destructive_calibration_check"],"neighboring_mechanisms":["reference_material_comparison","instrument_drift_control_chart"]},"operational_profile":{"strengths":["auditability","correction provenance","comparison support","maintenance trigger"],"limitations":["only as valid as reference applicability and chain","does not establish construct validity"],"tradeoffs":["calibration breadth and frequency versus cost and downtime"],"misuse_risks":["certificate as universal assurance","ignoring environment or range","treating traceability as accuracy"],"metrics_for_success":["complete links","calibration age","out-of-tolerance findings","uncertainty coverage"],"ethical_or_safety_notes":["Do not overstate formal traceability where only operational anchoring exists."]},"examples":{"examples":[{"domain":"metrology","example":"Analyzer calibration links each range through reference materials to an assigned unit with propagated uncertainty.","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["A sticker saying calibrated."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","metrology_review"],"notes":"Reconcile with Instrument Calibration Log and traceability records."}}

### Uncertainty Budget Table (`uncertainty_budget_table`)

**Type:** `artifact`

{"short_definition":"Lists uncertainty sources, estimates, distributions, sensitivity coefficients, correlations, contributions, and combined result.","expanded_definition":"Provides a reviewable bridge between the measurement model and the reported uncertainty or confidence statement.","mechanism_not_archetype_reason":"It quantifies one component of a designed measurement chain and cannot define the measurand, validate the method, or maintain operation by itself."}

Selection constraints and retained implementation evidence: consistent units; justified distributions or intervals; no material omissions or double counting; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"uncertainty_budget_table","name":"Uncertainty Budget Table","aliases":["measurement_uncertainty_contribution_table"]},"classification":{"mechanism_type":"artifact","domain_specificity":"cross_domain_with_adaptation","maturity":"common"},"definition":{"short_definition":"Lists uncertainty sources, estimates, distributions, sensitivity coefficients, correlations, contributions, and combined result.","expanded_definition":"Provides a reviewable bridge between the measurement model and the reported uncertainty or confidence statement.","mechanism_not_archetype_reason":"It quantifies one component of a designed measurement chain and cannot define the measurand, validate the method, or maintain operation by itself."},"implementation":{"operating_logic":"Enumerate material sources, assign magnitude and evidence, propagate through the model, account for correlation, combine, and document coverage interpretation.","typical_steps":["write measurement equation","inventory sources","classify evidence","estimate standard contributions","apply sensitivity coefficients","evaluate correlation","combine","expand or summarize","run sensitivity review"],"required_inputs":["measurement model","calibration evidence","repeatability data","sampling and environmental evidence"],"outputs_or_deliverables":["combined uncertainty","dominant-source analysis","coverage basis","improvement priorities"],"required_conditions":["consistent units","justified distributions or intervals","no material omissions or double counting"],"failure_conditions":["repeatability used as total uncertainty","correlations ignored","systematic terms omitted","interval meaning unclear"],"maintenance_or_update_needs":["update after method or environment changes","compare predictions with controls and duplicates"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"uncertainty quantification artifact"}],"supports_components":["uncertainty_budget","measurement_model","result_and_uncertainty_report"],"can_be_confused_with_archetypes":["uncertainty_explicitness"],"neighboring_mechanisms":["gauge_repeatability_reproducibility_study","calibration_traceability_record"]},"operational_profile":{"strengths":["transparent contributors","improvement targeting","qualified comparison","auditability"],"limitations":["model-dependent","difficult for poorly identified systematic or construct errors"],"tradeoffs":["analytic detail versus interpretability and maintenance"],"misuse_risks":["spurious numerical precision","omitting model validity","reporting one interval without semantics"],"metrics_for_success":["empirical coverage","residual discrepancy","dominant-source stability","review completeness"],"ethical_or_safety_notes":["Uncertainty should remain visible to downstream users and not be stripped for threshold convenience."]},"examples":{"examples":[{"domain":"engineering","example":"Sampling","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["Plus or minus one last displayed digit."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","uncertainty_specialist_review"],"notes":"Check for existing Uncertainty Budget Sheet."}}

### Gauge Repeatability and Reproducibility Study (`gauge_repeatability_reproducibility_study`)

**Type:** `test_or_assessment`

{"short_definition":"Separates same-condition repeat variation from operator, instrument, site, mode, or time variation.","expanded_definition":"Uses structured duplicate observations over representative targets and factor levels to quantify the measurement system's practical reproducibility.","mechanism_not_archetype_reason":"It is one validation assessment, not the whole measurement design and reporting lifecycle."}

Selection constraints and retained implementation evidence: stable targets or appropriate destructive design; representative range; independent repeats; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"gauge_repeatability_reproducibility_study","name":"Gauge Repeatability and Reproducibility Study","aliases":["measurement_system_repeatability_reproducibility_study"]},"classification":{"mechanism_type":"test_or_assessment","domain_specificity":"cross_domain_with_adaptation","maturity":"common"},"definition":{"short_definition":"Separates same-condition repeat variation from operator, instrument, site, mode, or time variation.","expanded_definition":"Uses structured duplicate observations over representative targets and factor levels to quantify the measurement system's practical reproducibility.","mechanism_not_archetype_reason":"It is one validation assessment, not the whole measurement design and reporting lifecycle."},"implementation":{"operating_logic":"Cross representative units with repeated observations and selected implementation factors, then estimate variance or agreement components relative to the use requirement.","typical_steps":["select representative range","define operators or modes","randomize order","repeat observations","preserve blinding","estimate components and interactions","compare with decision tolerance or uncertainty target","remediate and retest"],"required_inputs":["measurement protocol","representative targets","trained operators or modes","acceptance criteria"],"outputs_or_deliverables":["repeatability estimate","reproducibility estimate","factor interactions","fitness decision"],"required_conditions":["stable targets or appropriate destructive design","representative range","independent repeats"],"failure_conditions":["restricted range","order leakage","learning or drift confounded with operator","correlation mistaken for agreement"],"maintenance_or_update_needs":["repeat after major method","operator","site","instrument","or mode changes"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"precision and reproducibility assessment"}],"supports_components":["repeatability_reproducibility_profile","uncertainty_budget","rater_training_and_blinding"],"can_be_confused_with_archetypes":["measurement_protocol_standardization"],"neighboring_mechanisms":["interlaboratory_comparison","blinded_rater_assessment"]},"operational_profile":{"strengths":["factor separation","practical implementation evidence","targeted remediation"],"limitations":["does not establish validity or calibration","assumptions vary by scale and design"],"tradeoffs":["study size versus precision","representative range versus operational burden"],"misuse_risks":["using percentage thresholds mechanically","hiding systematic bias","treating high agreement as validity"],"metrics_for_success":["variance components","agreement limits","misclassification around thresholds","improvement after remediation"],"ethical_or_safety_notes":["Human-rater studies should protect workers and participants from punitive interpretation of method failures."]},"examples":{"examples":[{"domain":"manufacturing","example":"Multiple operators measure parts across the working range in randomized repeated order.","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["Two measurements of one easy sample by the same operator."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","agreement_statistics_review"],"notes":"Adapt terminology outside physical gauges."}}

### Reference Material Comparison (`reference_material_comparison`)

**Type:** `test_or_assessment`

{"short_definition":"Compares method results with a known, assigned, consensus, or benchmark reference over the relevant use range.","expanded_definition":"Tests bias, recovery, selectivity, matrix or context effects, linearity, and transfer against a reference whose own uncertainty and applicability are documented.","mechanism_not_archetype_reason":"It supplies calibration or validation evidence for one method but does not define the complete measurement system."}

Selection constraints and retained implementation evidence: reference stability and commutability; controlled handling; relevant range; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"reference_material_comparison","name":"Reference Material Comparison","aliases":["reference_standard_measurement_comparison"]},"classification":{"mechanism_type":"test_or_assessment","domain_specificity":"cross_domain_with_adaptation","maturity":"common"},"definition":{"short_definition":"Compares method results with a known, assigned, consensus, or benchmark reference over the relevant use range.","expanded_definition":"Tests bias, recovery, selectivity, matrix or context effects, linearity, and transfer against a reference whose own uncertainty and applicability are documented.","mechanism_not_archetype_reason":"It supplies calibration or validation evidence for one method but does not define the complete measurement system."},"implementation":{"operating_logic":"Select fit references, measure them under ordinary conditions, compare results to assignments with uncertainty, model bias, and decide correction or limitation.","typical_steps":["verify reference identity and applicability","select levels and matrices","randomize and replicate measurement","compare with assigned values","estimate bias and recovery","evaluate range and interference","update correction or validity boundary"],"required_inputs":["reference material or standard","measurement protocol","assigned value and uncertainty","acceptance criterion"],"outputs_or_deliverables":["bias and recovery estimates","calibration evidence","range limitation","comparison trace"],"required_conditions":["reference stability and commutability","controlled handling","relevant range"],"failure_conditions":["noncommutable reference","narrow range","reference uncertainty ignored","selective exclusion"],"maintenance_or_update_needs":["new lots","expiry","method changes","periodic checks","environmental expansion"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"reference validation mechanism"}],"supports_components":["reference_material_or_standard","calibration_and_traceability_chain","validity_and_selectivity_evidence"],"can_be_confused_with_archetypes":["non_destructive_calibration_check"],"neighboring_mechanisms":["calibration_traceability_record","interlaboratory_comparison"]},"operational_profile":{"strengths":["bias evidence","range assessment","transfer support","reference anchoring"],"limitations":["reference may not match real samples or constructs","assigned values have uncertainty"],"tradeoffs":["reference realism versus stability and cost"],"misuse_risks":["calibrating to an unrepresentative matrix","treating benchmark performance as universal validity"],"metrics_for_success":["bias by level","recovery","residuals","uncertainty-normalized error","lot stability"],"ethical_or_safety_notes":["Reference populations and benchmark datasets can encode inequity or exclusion and need governance."]},"examples":{"examples":[{"domain":"chemistry","example":"Matrix-matched certified materials span the assay's intended concentration range.","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["One convenient sample assumed to be correct."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","reference_applicability_review"],"notes":"Reconcile with existing transfer-standard comparison mechanisms."}}

### Instrument Drift Control Chart (`instrument_drift_control_chart`)

**Type:** `metric_or_dashboard`

{"short_definition":"Tracks reference or control results over time and triggers hold, investigation, correction, or recalibration.","expanded_definition":"Separates expected control variation from shifts, trends, jumps, cyclic effects, and instability tied to instrument, lot, operator, environment, or software.","mechanism_not_archetype_reason":"It monitors one operational performance dimension after measurement design and validation."}

Selection constraints and retained implementation evidence: control sensitivity to relevant failure; timely review; authority to hold results; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"instrument_drift_control_chart","name":"Instrument Drift Control Chart","aliases":["measurement_control_chart"]},"classification":{"mechanism_type":"metric_or_dashboard","domain_specificity":"cross_domain_with_adaptation","maturity":"common"},"definition":{"short_definition":"Tracks reference or control results over time and triggers hold, investigation, correction, or recalibration.","expanded_definition":"Separates expected control variation from shifts, trends, jumps, cyclic effects, and instability tied to instrument, lot, operator, environment, or software.","mechanism_not_archetype_reason":"It monitors one operational performance dimension after measurement design and validation."},"implementation":{"operating_logic":"Measure stable controls at a declared cadence, chart results against evidence-based limits, apply nonrandom-pattern rules, and bind alerts to release control.","typical_steps":["select controls","establish baseline and limits","schedule observations","record context","apply run and trend rules","hold affected results","investigate root cause","correct and document release"],"required_inputs":["stable control","baseline distribution","measurement protocol","release and escalation rules"],"outputs_or_deliverables":["control chart","drift alert","affected-result window","corrective-action record"],"required_conditions":["control sensitivity to relevant failure","timely review","authority to hold results"],"failure_conditions":["limits tuned to avoid alerts","control not commutable","environmental pattern ignored","alert without containment"],"maintenance_or_update_needs":["baseline review after validated change","control lot bridging","rule performance review"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"operational drift monitoring mechanism"}],"supports_components":["quality_control_and_drift_monitor","instrument_and_sensor_profile"],"can_be_confused_with_archetypes":["adaptive_threshold_recalibration"],"neighboring_mechanisms":["reference_material_comparison","calibration_traceability_record"]},"operational_profile":{"strengths":["early drift detection","affected-window containment","temporal evidence"],"limitations":["only detects changes represented by controls","can overreact to autocorrelation or lot changes"],"tradeoffs":["sensitivity versus false holds","cadence versus cost"],"misuse_risks":["ignoring trends inside limits","shifting limits after failure","releasing results before investigation"],"metrics_for_success":["detection delay","false alarm rate","invalid results contained","corrective action recurrence"],"ethical_or_safety_notes":["Production pressure must not override quality-control holds in high-consequence measurement."]},"examples":{"examples":[{"domain":"environmental_sensing","example":"Reference checks chart sensor bias and hold data after a sustained shift.","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["A dashboard trend with no control material or action rule."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","statistical_process_control_review"],"notes":"Reconcile with existing control-chart drift monitoring."}}

### Blinded Rater Assessment (`blinded_rater_assessment`)

**Type:** `procedure`

{"short_definition":"Uses trained observers shielded from irrelevant expectation, identity, treatment, outcome, or prior-score information.","expanded_definition":"Produces independent ratings under a rubric, preserves disagreement, and separates observer effects from target differences.","mechanism_not_archetype_reason":"It is one human-instrument procedure inside rating-based measurement."}

Selection constraints and retained implementation evidence: masking integrity; safe workload; independent initial judgment; usable rubric; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"blinded_rater_assessment","name":"Blinded Rater Assessment","aliases":["masked_observer_rating_protocol"]},"classification":{"mechanism_type":"procedure","domain_specificity":"cross_domain_with_adaptation","maturity":"common"},"definition":{"short_definition":"Uses trained observers shielded from irrelevant expectation, identity, treatment, outcome, or prior-score information.","expanded_definition":"Produces independent ratings under a rubric, preserves disagreement, and separates observer effects from target differences.","mechanism_not_archetype_reason":"It is one human-instrument procedure inside rating-based measurement."},"implementation":{"operating_logic":"Define masking scope, prepare de-identified observation material, randomize assignment and order, collect independent ratings, test agreement, and adjudicate separately.","typical_steps":["identify biasing information","create masking and access plan","train on anchors","randomize cases","collect independent ratings","preserve confidence and abstentions","analyze agreement","adjudicate unresolved cases"],"required_inputs":["operational rubric","trained raters","masking plan","representative cases"],"outputs_or_deliverables":["independent ratings","agreement profile","disagreement and adjudication record"],"required_conditions":["masking integrity","safe workload","independent initial judgment","usable rubric"],"failure_conditions":["masking broken","group discussion before rating","forced certainty","differential case context"],"maintenance_or_update_needs":["rater drift checks","retraining","anchor refresh","workload review"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"human-rating bias control mechanism"}],"supports_components":["rater_training_and_blinding","repeatability_reproducibility_profile","validity_and_selectivity_evidence"],"can_be_confused_with_archetypes":["blinding_and_expectancy_bias_reduction"],"neighboring_mechanisms":["gauge_repeatability_reproducibility_study","measurement_protocol"]},"operational_profile":{"strengths":["reduces expectancy effects","exposes independent disagreement","supports reproducibility"],"limitations":["some context is necessary for valid judgment","masking may be impossible or ethically inappropriate"],"tradeoffs":["bias reduction versus contextual validity","duplicate rating versus burden"],"misuse_risks":["masking relevant safety context","punishing raters for disagreement","adjudication erasing uncertainty"],"metrics_for_success":["masking success","agreement by subgroup and case type","abstention","adjudication frequency"],"ethical_or_safety_notes":["Protect participant identity and rater working conditions; do not conceal information needed for safety."]},"examples":{"examples":[{"domain":"clinical_imaging","example":"Readers score de-identified images without treatment or outcome information before adjudication.","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["Raters know expected outcomes and discuss cases until one score remains."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","human_rater_ethics_review"],"notes":"Reconcile with existing blinded assessment scripts."}}

### Limit of Detection Estimation (`limit_of_detection_estimation`)

**Type:** `test_or_assessment`

{"short_definition":"Establishes the observation range where signal can be distinguished from blank, background, noise, or quantification limits.","expanded_definition":"Uses blank and low-level distributions, detection criteria, false positive/negative consequences, and method variability to define detection and quantification states.","mechanism_not_archetype_reason":"It characterizes one boundary of a measurement method rather than the full measurement lifecycle."}

Selection constraints and retained implementation evidence: representative background; stable method; enough observations; explicit decision criterion; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"limit_of_detection_estimation","name":"Limit of Detection Estimation","aliases":["detection_and_quantification_limit_study"]},"classification":{"mechanism_type":"test_or_assessment","domain_specificity":"cross_domain_with_adaptation","maturity":"specialized"},"definition":{"short_definition":"Establishes the observation range where signal can be distinguished from blank, background, noise, or quantification limits.","expanded_definition":"Uses blank and low-level distributions, detection criteria, false positive/negative consequences, and method variability to define detection and quantification states.","mechanism_not_archetype_reason":"It characterizes one boundary of a measurement method rather than the full measurement lifecycle."},"implementation":{"operating_logic":"Collect representative blank and low-level observations, define error criteria, estimate detection and quantification boundaries, verify them, and encode result states.","typical_steps":["define blank and background","choose error or signal criterion","collect repeated observations","model distributions","estimate limits","verify with independent low-level samples","define reporting codes and uncertainty"],"required_inputs":["measurement protocol","blank or background samples","low-level references","consequence criteria"],"outputs_or_deliverables":["detection limit","quantification limit","false-result characterization","reporting policy"],"required_conditions":["representative background","stable method","enough observations","explicit decision criterion"],"failure_conditions":["theoretical instrument limit used as system limit","matrix ignored","nonstationary background","below-limit values reported as exact"],"maintenance_or_update_needs":["re-estimate after method","matrix","environment","software","or instrument change"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"observation-boundary assessment"}],"supports_components":["missing_censoring_and_detection_limit_policy","instrument_and_sensor_profile","uncertainty_budget"],"can_be_confused_with_archetypes":[],"neighboring_mechanisms":["reference_material_comparison","measurement_system_validation_study"]},"operational_profile":{"strengths":["honest low-range boundary","explicit false-result tradeoff","correct result states"],"limitations":["model and matrix dependent","may vary over time and environment"],"tradeoffs":["sensitivity versus false positives","quantification certainty versus usable range"],"misuse_risks":["replacing below detection with zero","comparing estimates below quantification as ordinary values"],"metrics_for_success":["verified detection probability","false positive rate","low-level recovery","boundary stability"],"ethical_or_safety_notes":["Threshold consequences should match the cost of missed and false detections."]},"examples":{"examples":[{"domain":"environmental_assay","example":"Field-matrix blanks and low spikes establish detected","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["Use the smallest digit shown on the device."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","detection_limit_review"],"notes":"Reconcile with existing detection-limit mechanisms."}}

### Interlaboratory Comparison (`interlaboratory_comparison`)

**Type:** `test_or_assessment`

{"short_definition":"Tests reproducibility, equivalence, and unexplained implementation effects across independent sites, methods, instruments, or teams.","expanded_definition":"Distributes common or comparable targets, preserves independent processing, compares results with uncertainty, and distinguishes site bias from target heterogeneity.","mechanism_not_archetype_reason":"It validates transfer and reproducibility for an existing measurement design rather than defining the whole chain."}

Selection constraints and retained implementation evidence: target comparability; independent execution; transparent method metadata; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"interlaboratory_comparison","name":"Interlaboratory Comparison","aliases":["cross_site_measurement_comparison"]},"classification":{"mechanism_type":"test_or_assessment","domain_specificity":"cross_domain_with_adaptation","maturity":"common"},"definition":{"short_definition":"Tests reproducibility, equivalence, and unexplained implementation effects across independent sites, methods, instruments, or teams.","expanded_definition":"Distributes common or comparable targets, preserves independent processing, compares results with uncertainty, and distinguishes site bias from target heterogeneity.","mechanism_not_archetype_reason":"It validates transfer and reproducibility for an existing measurement design rather than defining the whole chain."},"implementation":{"operating_logic":"Select comparison targets, specify common information without overstandardizing away local reality, collect independent results and uncertainty, analyze differences, and remediate.","typical_steps":["define comparison objective","select stable representative targets","distribute or coordinate observations","preserve independent execution","collect qualified results","analyze bias and dispersion","investigate outliers","document harmonization or limits"],"required_inputs":["participating sites or methods","comparable targets","result schema","comparison analysis plan"],"outputs_or_deliverables":["site or method effects","reproducibility profile","harmonization evidence","corrective actions"],"required_conditions":["target comparability","independent execution","transparent method metadata"],"failure_conditions":["sample instability","selective participant exclusion","method differences hidden","consensus mean treated as truth"],"maintenance_or_update_needs":["periodic rounds","new method/site onboarding","corrective-action follow-up"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"cross-implementation reproducibility mechanism"}],"supports_components":["repeatability_reproducibility_profile","interoperability_and_harmonization_crosswalk","measurement_stewardship_and_versioning"],"can_be_confused_with_archetypes":["measurement_protocol_standardization"],"neighboring_mechanisms":["gauge_repeatability_reproducibility_study","reference_material_comparison"]},"operational_profile":{"strengths":["real-world transfer evidence","hidden site effects","harmonization support"],"limitations":["logistics and target stability","consensus values may be biased"],"tradeoffs":["common protocol versus realistic implementation diversity"],"misuse_risks":["ranking sites without uncertainty","hiding method heterogeneity","treating peer consensus as traceable truth"],"metrics_for_success":["between-site dispersion","uncertainty-normalized deviations","corrective-action closure","reproducibility improvement"],"ethical_or_safety_notes":["Use results for method improvement rather than punitive exposure without due process."]},"examples":{"examples":[{"domain":"public_health_laboratories","example":"Independent sites measure common specimens and compare qualified results across methods.","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["Sites copy one another's results to force agreement."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","proficiency_testing_review"],"notes":"Reconcile with protocol equivalence mechanisms."}}

### Measurement System Validation Study (`measurement_system_validation_study`)

**Type:** `workflow`

{"short_definition":"Integrates construct, range, selectivity, calibration, precision, robustness, uncertainty, transfer, and use-boundary evidence.","expanded_definition":"Tests the complete measurement claim against predeclared fitness criteria and produces an approved, restricted, revised, or rejected use decision.","mechanism_not_archetype_reason":"It is a finite validation workflow instantiated within the broader lifecycle, which also includes design, routine operation, reporting, monitoring, and change control."}

Selection constraints and retained implementation evidence: independent review; representative evidence; predeclared criteria; access to raw and method provenance; Complete legacy mechanism record retained: {"metadata":{"schema_version":"1.0","artifact_type":"solution_mechanism","status":"draft","source":"phase_02_related_only_coverage_batch_002/traceable_measurement_system_design"},"identity":{"slug":"measurement_system_validation_study","name":"Measurement System Validation Study","aliases":["measurement_method_fit_for_use_validation"]},"classification":{"mechanism_type":"workflow","domain_specificity":"cross_domain_with_adaptation","maturity":"specialized"},"definition":{"short_definition":"Integrates construct, range, selectivity, calibration, precision, robustness, uncertainty, transfer, and use-boundary evidence.","expanded_definition":"Tests the complete measurement claim against predeclared fitness criteria and produces an approved, restricted, revised, or rejected use decision.","mechanism_not_archetype_reason":"It is a finite validation workflow instantiated within the broader lifecycle, which also includes design, routine operation, reporting, monitoring, and change control."},"implementation":{"operating_logic":"Translate intended use into validation claims, test each measurement-chain link with independent evidence, stress boundaries and rival explanations, and govern release.","typical_steps":["state measurand and use claims","define acceptance criteria","audit measurement model","test range and selectivity","verify calibration and traceability","estimate precision and uncertainty","test robustness and transfer","assess subgroup or context performance","document residual risks","approve or restrict use"],"required_inputs":["complete draft measurement design","intended-use criteria","representative targets and contexts","independent validation plan"],"outputs_or_deliverables":["validation report","approved use boundary","residual risk register","remediation plan","revalidation triggers"],"required_conditions":["independent review","representative evidence","predeclared criteria","access to raw and method provenance"],"failure_conditions":["validation on development data only","weak comparator","restricted range","failed criteria waived silently","no subgroup review"],"maintenance_or_update_needs":["revalidate after material method","population","environment","instrument","software","or use change"]},"archetype_links":{"instantiates_archetypes":[{"slug":"traceable_measurement_system_design","role":"integrated fitness-for-use validation workflow"}],"supports_components":["intended_use_and_decision_link","validity_and_selectivity_evidence","uncertainty_budget","measurement_stewardship_and_versioning"],"can_be_confused_with_archetypes":["measurement_protocol_standardization"],"neighboring_mechanisms":["reference_material_comparison","gauge_repeatability_reproducibility_study","interlaboratory_comparison"]},"operational_profile":{"strengths":["end-to-end evidence","explicit use boundary","cross-disciplinary review","revalidation triggers"],"limitations":["resource intensive","cannot anticipate every future context","quality depends on independent evidence"],"tradeoffs":["validation breadth versus time and cost","conservative boundary versus usability"],"misuse_risks":["approval theater","waiver of failed criteria","expanding use without revalidation","ignoring ethical invalidity"],"metrics_for_success":["criteria passed","residual risk closure","postdeployment validity","revalidation timeliness"],"ethical_or_safety_notes":["High-stakes measurement requires affected-population","privacy","fairness","and appeal review in addition to technical validation."]},"examples":{"examples":[{"domain":"clinical_measurement","example":"A study validates construct","archetype_instantiated":"traceable_measurement_system_design"}],"non_examples":["A vendor brochure saying the instrument is validated."]},"review":{"review_status":"not_reviewed","review_flags":["mechanism_extraction_review","multidisciplinary_validation_review"],"notes":"Reconcile with accepted validation mechanisms."}}

prose_failure_modes:
**Wrong measurand:** the value answers a convenient question rather than the decision question.
Mitigate by tracing the intended outcome, target attribute, operational definition, proxy assumptions,
and consequence before instrument selection.

**False precision:** display resolution or replicate consistency is reported as total confidence.
Mitigate with an uncertainty budget covering calibration, sampling, environment, model, rater,
reduction, and transfer contributions, plus appropriate significant digits.

**Calibration mismatch:** reference evidence covers another range, mode, environment, configuration,
or time. Mitigate with use-envelope calibration, control checks, interpolation boundaries, and an
explicit hold on extrapolated results.

**Sampling bias:** observed cases, sites, times, modes, or specimens do not represent the target.
Mitigate with a sampling frame, inclusion probabilities or justified design, duplicates, coverage
review, and a narrowed inference boundary.

**Proxy-target drift:** the surrogate relationship changes while the proxy continues to improve.
Mitigate with direct target checks, fidelity monitoring, rival indicators, anti-gaming review, and a
retirement trigger.

**Rater or mode effect:** observer expectation, training, language, interface, or administration
changes results. Mitigate with blinding, training, anchors, duplicate ratings, agreement analysis,
mode-equivalence evidence, and adjudication.

**Hidden censoring:** below-detection, saturation, missing, and invalid observations become zeros or
ordinary values. Mitigate with state-specific codes, retained raw signals, declared estimation rules,
and uncertainty-aware reporting.

**Method drift:** instrument, software, reference, scoring, or environment changes gradually.
Mitigate with controls, drift charts, change review, overlap studies, versioning, and historical break
annotations.

**Measurement reactivity:** observation changes the participant, specimen, system load, or behavior.
Mitigate with disturbance budgets, nonintrusive methods, blinded or randomized observation, burden
limits, and explicit reactivity uncertainty.

**Invalid transfer:** values are compared across incompatible units, scales, frames, populations,
methods, or versions. Mitigate with a validated harmonization crosswalk or refuse direct comparison.

prose_neighbor_distinctions:
Measurement-Protocol Standardization assumes a selected measurement and focuses on comparability:
same construct, instruments, administration, timing, scoring, calibration, and deviations across
subjects or sites. Traceable Measurement System Design precedes and contains that decision context by
establishing whether the construct, scale, instrument, model, sampling, traceability, uncertainty, and
validity can support the use at all.

Observability Instrumentation asks which signals make hidden operational state inferable for monitoring
and control. Those signals may become measurements, but an observability design can remain useful
without formal units, traceability, uncertainty, or cross-context comparability. Conversely,
measurement can concern directly accessible attributes unrelated to control.

Non-Destructive Calibration Check verifies an existing live measurement relationship against
independent evidence. Coverage Probability Calibration verifies intervals. Aggregation archetypes
combine or summarize measures. Control-Condition Specification defines a causal comparator. Scoped
Experimentation bounds exposure while learning. Each is a consumer, maintainer, or specialist neighbor,
not the general measurement-chain parent.

prose_cross_domain_examples:
**Laboratory concentration:** The measurand specifies analyte, matrix, temperature, and preparation.
Reference materials establish calibration and recovery across range. Blanks, duplicates, control
charts, detection limits, and uncertainty contributions accompany each reported concentration.

**Clinical symptom scale:** The construct and intended change decision are defined. Items, language,
response options, administration, scoring, missingness, reliability, construct validity, rater or
self-report effects, and meaningful-change uncertainty are maintained by version.

**Environmental sensor network:** Spatial and temporal sampling determine what region and interval the
measure represents. Reference monitors, co-location calibration, humidity correction, drift controls,
missing data, sensor replacement, and model uncertainty travel with published concentrations.

**Software latency:** Start and stop events, request population, clock synchronization, sampling,
timeouts, censored requests, retries, percentile reduction, aggregation window, and confidence are
specified. A dashboard digit without these semantics is not treated as the measurement definition.

prose_non_examples:
- A bare number exported by an instrument with no unit, method, frame, or uncertainty.
- A dashboard signal installed only to alert operators, with no claim of traceable measurement.
- A valid scale administered differently across sites; that is primarily protocol standardization.
- A composite score built from several measures; that is aggregation design after input validation.
- A field calibration spot-check on an already defined instrument; that is Non-Destructive Calibration Check.
- A causal control arm specification that uses measurements but does not create them.
- A qualitative account forced into numeric categories solely because a dashboard requires a number.

prose_tradeoffs:
Precision, validity, representativeness, speed, cost, burden, intrusion, and interpretability cannot
all be maximized. More samples can reduce sampling uncertainty but increase participant burden and
delay. More sensitive instruments can be less robust outside controlled environments. More
standardization can erase context. More ecological realism can reduce control and repeatability.

Continuous high-resolution sensing improves temporal visibility while increasing noise, data volume,
privacy exposure, and drift. Direct measurement can disturb or destroy the target; proxies preserve it
but add fidelity risk. Formal traceability strengthens comparability but may not exist for social or
emerging constructs, where transparent validation and reference-population choices are more honest.

### Ethical And Safety Considerations

Measurement creates authority. Scores and readings can determine diagnosis, employment, benefits,
inspection, policing, resource access, sanctions, and public narratives. The measurand and threshold
are therefore governance choices as well as technical choices. A reliable instrument can measure an
unjust construct consistently.

Collect only evidence proportionate to the use. Explain purpose, burden, retention, access, secondary
use, correction, and appeal. Protect raw traces, location, behavior, health, and identity data.
Review disparate validity and error across groups rather than assuming one aggregate calibration is
universal. Do not hide uncertainty to make automated decisions appear objective.

Measurement can induce gaming and proxy substitution. When a measure becomes a target, maintain
independent outcome checks and watch for burden shifting, strategic omission, and distributional
harm. Preserve a human route for context the scale cannot represent, and allow “not measurable with
this method” to remain a legitimate conclusion.

prose_invariants_to_preserve:
The reported value always points back to one versioned measurand, method, scale, frame, procedure,
sample, and time. Raw indications and transformation provenance remain reconstructable where
practicable. Calibration and reference evidence matches the actual use envelope. Uncertainty remains
attached to the result and is not replaced by display digits, tolerance, or repeatability alone.

Proxy evidence remains labeled. Construct definitions and reference populations remain visible.
Missingness and detection states retain their semantics. Method changes cannot silently preserve the
same series label without overlap, crosswalk, or break annotation. A failed quality control can block
release. High stakes do not manufacture certainty; they raise the evidence requirement or force a
safer decision process.

prose_target_outcomes:
The measurement answers a named question about a defined attribute. Users can interpret the value,
compare it only where appropriate, reproduce or audit the method, and see which uncertainty and
validity limits matter. Calibration, sampling, rater, proxy, environment, data-reduction, and drift
effects become visible before they harden into false conclusions.

Historical continuity improves because every result carries method version and crosswalk evidence.
Organizations can separate actual change in the target from change in instrument or procedure.
Decision thresholds can use uncertainty rather than pretending observations are exact. Invalid or
inconclusive measurements are detected and contained rather than laundered into ordinary values.

prose_parameter_dimensions:
- **Measurand type:** physical quantity, chemical or biological analyte, latent construct,
  observed behavior, operational performance, exposure, categorical state, or derived index.
- **Access path:** direct instrument response, proxy, self-report, observer rating, assay,
  administrative record, computational estimate, or fused system.
- **Scale semantics:** nominal, ordinal, interval, ratio, bounded score, count, probability,
  concentration, rate, duration, spatial field, or multidimensional profile.
- **Reference frame:** physical frame, baseline, population norm, coordinate system, unit realization,
  instrument reference, policy definition, or historical method.
- **Sampling:** census or sample; random, stratified, purposive, continuous, event-triggered, spatial,
  temporal, destructive, repeated, or adaptive.
- **Instrument:** physical device, sensor array, assay, rater, rubric, questionnaire, algorithm,
  parser, clock, or combined chain.
- **Traceability:** formal metrological chain, reference material, consensus standard, calibrated
  transfer method, operational baseline, or documented non-traceable limitation.
- **Uncertainty expression:** standard or expanded uncertainty, interval, posterior distribution,
  classification probability, inter-rater disagreement, sensitivity range, or qualitative grade.
- **Decision stakes:** exploration, monitoring, screening, diagnosis, control, certification,
  enforcement, payment, scientific inference, or public communication.

prose_variants_and_near_names:
Direct Instrumental Measurement uses an instrument model and calibration to realize a quantity.
Proxy-Based Measurement introduces a maintained surrogate-target fidelity model. Observational Coding
and Rating uses trained human judgment and therefore emphasizes rubrics, blinding, and agreement.
Self-Report Measurement relies on first-person access and must manage language, recall, demand,
response options, and safety.

Destructive Sampling Measurement consumes or alters the unit, making representativeness, custody,
preparation, and irreversibility central. Remote or Continuous Sensing adds streaming, synchronization,
drift, missingness, data volume, surveillance, and adaptive sampling. All variants preserve the same
measurand-to-qualified-result structure.

Direct Instrumental Measurement

variant_type: other

short_definition: A physical, chemical, biological, or computational instrument responds comparatively directly to the specified attribute through a calibrated measurement model. The result remains model-dependent but does not rely primarily on a separate proxy construct or human interpretive rating.

use_when: The attribute has a defensible instrument response and unit or scale realization.

use_when: Reference evidence and use-envelope calibration are available.

use_when: Instrument loading, selectivity, environment, and drift can be bounded.

common_components: Define sensitivity, selectivity, range, resolution, linearity, hysteresis, and saturation.

common_components: Establish calibration and traceability for the actual configuration and environment.

common_components: Separate instrument repeatability from sampling and model uncertainty.

common_components: Monitor blanks, controls, references, drift, and maintenance effects.

example_domains: The attribute has a defensible instrument response and unit or scale realization.

example_domains: Reference evidence and use-envelope calibration are available.

example_domains: Instrument loading, selectivity, environment, and drift can be bounded.

failure_modes: display resolution mistaken for accuracy

Proxy-Based Measurement

short_definition: An accessible surrogate, marker, score, or signal is mapped to a target that cannot be observed directly. The surrogate-target relationship and its stability become explicit parts of validity and uncertainty rather than background assumptions.

use_when: Direct measurement is impossible, delayed, destructive, expensive, unsafe, or unethical.

use_when: A target-proxy model can be validated in the intended population and regime.

use_when: Independent target evidence can periodically test fidelity and drift.

common_components: Name the target and proxy separately and state the causal or empirical bridge.

common_components: Quantify fidelity, subgroup variation, transport limits, and proxy-target divergence.

common_components: Monitor gaming, feedback, regime change, and decoupling after deployment.

common_components: Preserve direct or rival indicators as a check where feasible.

example_domains: Direct measurement is impossible, delayed, destructive, expensive, unsafe, or unethical.

example_domains: A target-proxy model can be validated in the intended population and regime.

example_domains: Independent target evidence can periodically test fidelity and drift.

failure_modes: proxy optimization without target improvement

Observational Coding and Rating

short_definition: Trained human judgment converts observed behavior, performance, images, records, or artifacts into categories or scale values through an operational definition and rubric. Rater effects and adjudication are instrument properties rather than incidental noise.

use_when: Relevant attributes require contextual interpretation not captured by direct sensors.

use_when: A rubric, anchor examples, training, and duplicate coding can stabilize judgments.

use_when: Blinding and conflict controls can reduce expectancy or differential treatment.

common_components: Define observable criteria, category boundaries, anchors, and abstain or inconclusive states.

common_components: Train raters and assess intra-rater and inter-rater agreement.

common_components: Blind raters to irrelevant outcome, treatment, identity, or expectation information.

common_components: Preserve disagreement, adjudication, and version changes in the record.

example_domains: Relevant attributes require contextual interpretation not captured by direct sensors.

example_domains: A rubric, anchor examples, training, and duplicate coding can stabilize judgments.

example_domains: Blinding and conflict controls can reduce expectancy or differential treatment.

failure_modes:
rater drift
expectancy bias
differential interpretation
forced coding
adjudication hiding disagreement

Self-Report Measurement

short_definition: Participants provide structured evidence about their own experience, belief, behavior, symptoms, or condition. First-person access is an evidential strength, while recall, language, response format, demand, safety, and willingness shape the measurement model.

use_when: The target includes subjective experience or private information available to the respondent.

use_when: Items and response scales can be validated for language, population, and mode.

use_when: Privacy, voluntariness, and safe nonresponse can be protected.

common_components: Define construct domains and item-to-construct rationale.

common_components: Test comprehension, response-process validity, reliability, and subgroup performance.

common_components: Distinguish nonresponse, not applicable, prefer not to answer, and true scale values.

common_components: Control mode, ordering, demand, recall period, translation, and accessibility effects.

example_domains: The target includes subjective experience or private information available to the respondent.

example_domains: Items and response scales can be validated for language, population, and mode.

example_domains: Privacy, voluntariness, and safe nonresponse can be protected.

failure_modes:
recall bias
social desirability
unsafe disclosure
translation drift
response-scale misuse
missingness treated as zero

Destructive Sampling Measurement

short_definition: Specimen preparation, assay, disassembly, stress-to-failure, or another procedure consumes, alters, or damages the observed unit. Sampling, custody, preparation, representativeness, and irreversibility become part of the measurement contract.

use_when: The required attribute cannot be obtained non-destructively with adequate validity.

use_when: A representative sample can stand for the larger target with documented uncertainty.

use_when: Ethical, safety, scarcity, and chain-of-custody controls permit consumption.

common_components: Define sampling frame, specimen selection, custody, preservation, and preparation.

common_components: Quantify within-target heterogeneity and sampling uncertainty.

common_components: Use blanks, spikes, controls, duplicates, and recovery evidence as appropriate.

common_components: Predefine retention, retest, disposal, and no-repeat consequences.

example_domains: The required attribute cannot be obtained non-destructively with adequate validity.

example_domains: A representative sample can stand for the larger target with documented uncertainty.

example_domains: Ethical, safety, scarcity, and chain-of-custody controls permit consumption.

failure_modes:
unrepresentative specimen
preparation contamination
custody break
depletion of rare material
survivorship bias

Remote or Continuous Sensing

short_definition: Automated instruments repeatedly collect indications across time or space. Synchronization, temporal resolution, spatial placement, data loss, drift, replacement, aggregation, alerting, and surveillance become central design dimensions.

use_when: Dynamics, events, or spatial variation cannot be captured by sparse manual observations.

use_when: Sensors can be calibrated, synchronized, maintained, and monitored at deployment scale.

use_when: Data volume, access, retention, and privacy are governed.

common_components: Define sampling cadence, event triggers, clocks, aggregation windows, and raw retention.

common_components: Track sensor identity, location, firmware, calibration, drift, replacement, and outages.

common_components: Distinguish missingness, offline state, saturation, and genuine zero or stable state.

common_components: Evaluate observation burden, behavioral reactivity, security, and secondary use.

example_domains: Dynamics, events, or spatial variation cannot be captured by sparse manual observations.

example_domains: Sensors can be calibrated, synchronized, maintained, and monitored at deployment scale.

example_domains: Data volume, access, retention, and privacy are governed.

failure_modes:
clock misalignment
silent outage
placement bias
correlated drift
overaggregation
surveillance overreach