{"schema_version":1,"assessment_id":"eoa_inverse_innovation_exp03_opportunity320_20260801","source_experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"computability_boundary_mapping__sport_science","archetype_slug":"computability_boundary_mapping","domain_slug":"sport_science","title":"Model-Relative Verification Boundaries for Adaptive Training Safety","opportunity_summary":"Classify the computability and scope of safety-verification claims for executable athlete-model and adaptive-training-controller pairs, route only supported instances to bounded verification methods, and preserve UNKNOWN, TIMEOUT, and OUT_OF_SCOPE rather than converting incomplete analysis into safety clearance. The opportunity is conditional because the sealed record does not establish that sport-science teams actually demand or deploy unrestricted universal verification.","adopter_authorizer":"Sport-science and verification-software teams could adopt the classification and routing workflow, while the applicable sports-medicine or research-governance authority retains authorization over athlete exposure and training changes.","scores":{"meaningful_impact":{"score":3,"rationale":"Preventing model-relative analysis from being mistaken for real-world safety could reduce false clearance, blanket rejection, and wasted verifier development. Impact remains uncertain because the sealed candidate does not establish incidence, frequency, or consequential use of unrestricted verification claims."},"stakeholder_pull":{"score":2,"rationale":"The candidate identifies plausible users and affected athletes but supplies no observed requests, specifications, procurement interest, or evidence that teams currently face the proposed universal-verification problem."},"incremental_advantage":{"score":3,"rationale":"Boundary classification, enforceable fragment routing, and explicit UNKNOWN states address guarantee validity that the stated simulation baseline and predictive-validation rival do not address. The advantage is conditional on open-ended model classes being admitted and on routing improving false-SAFE or ambiguity outcomes without destroying utility."},"distinctiveness_plausibility":{"score":2,"rationale":"The sport-science composition is coherent, but the record is explicitly unsearched and provides no comparison with formal verification, hybrid-systems safety, or model-governance methods. Distinctiveness therefore cannot be credited beyond a plausible domain-specific composition."},"technical_implementability":{"score":3,"rationale":"An offline inventory, scope checker, label router, and bounded model-library evaluation are implementable in principle. Faithful requirement formalization, enforceable fragment membership, checked impossibility arguments, and useful conservative abstractions remain unresolved."},"adoption_authority_feasibility":{"score":4,"rationale":"The candidate clearly separates verifier ownership from athlete-exposure authority, names sports-medicine or research governance as authorizer, and provides a non-interventional shadow-test path with explicit excluded actions and withdrawal conditions."},"evidence_readiness":{"score":3,"rationale":"The proposal supplies separate problem and intervention falsifiers, an authorized shadow setting, a comparator, and halt criteria. It lacks an assembled model library, operational label metrics, a fixed usable-answer threshold, stakeholder requirements, and independently reviewed proofs."},"safety_net_benefit":{"score":4,"rationale":"Preserving UNKNOWN, TIMEOUT, and OUT_OF_SCOPE, blocking autonomous prescription changes, and withdrawing the guarantee after any unsafe bounded case is labeled SAFE provide a strong procedural safety net. These measures cannot correct an invalid athlete model or prohibited-state predicate."},"scalability":{"score":3,"rationale":"A common router and evidence-label scheme could be reused across teams, but each model language, controller class, horizon, predicate, and model revision may require new classification, proof review, and governance work."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"10K_TO_50K","scope":"A bounded requirements and interface inventory plus protocol design for an offline comparison using synthetic and previously reviewed models, with no athlete exposure or new athlete data.","confidence":"LOW","assumptions":["A limited number of cooperating sport-science and software stakeholders participate.","Existing specifications and reviewed models are accessible without costly data licensing.","The work uses a small interdisciplinary team and does not attempt a complete impossibility proof."]},"initial_deployment_startup":{"band_2026_usd":"50K_TO_250K","scope":"Build a prototype scope classifier and router, curate the finite test library, implement explicit evidence labels, conduct formal and governance review, and run the shadow comparison.","confidence":"LOW","assumptions":["The admitted model languages and interfaces can be documented.","Existing simulation infrastructure can be adapted rather than replaced.","No regulated clinical deployment, athlete intervention, or bespoke hardware is included."]},"operational_launch":{"band_2026_usd":"250K_TO_1M","scope":"Integrate scope enforcement and evidence labels into one organization's modeling workflow, validate access controls and audit records, train operators, independently review guarantees, and complete launch governance.","confidence":"LOW","assumptions":["Launch is limited to one organization and a defined set of model fragments.","The system remains advisory and cannot authorize training changes.","Substantial redesign is unnecessary after the shadow test."]},"annual_recurring":{"band_2026_usd":"50K_TO_250K","scope":"Maintain the router and fragment definitions, reclassify model changes, review proofs and exceptions, monitor scope bypasses, support users, and perform periodic governance audits.","confidence":"LOW","assumptions":["Deployment remains within one organization or a small stable program.","Model and controller changes occur at a manageable rate.","No continuous large-scale data acquisition or athlete monitoring is added."]}},"research_burden":"HIGH","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"UNCERTAIN","reason":"The candidate describes a precise and falsifiable failure mode, but states only that teams may require such an analyzer and supplies no external evidence that open-ended universal claims or timeout-derived verdicts occur in practice."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"Sport-science and software teams are identifiable adopters, and sports-medicine or research-governance personnel are explicitly assigned athlete-exposure authority."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The candidate claims that boundary mapping and routing will reduce false-SAFE labels or guarantee ambiguity relative to simulation or conventional predictive validation, subject to a predeclared usable-answer threshold."},"bounded_next_evidence_step":{"status":"YES","reason":"A finite, non-interventional inventory and shadow evaluation can compare claimed guarantees and router labels against exhaustive within-bound results without changing training."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The first step excludes athlete exposure and autonomous prescription, preserves non-safe fallback states, retains independent authority, and includes explicit halt and withdrawal conditions."},"implementation_cost_scope_and_range":{"status":"UNCERTAIN","reason":"Broad activities can be scoped, but the number and complexity of model languages, existing infrastructure, proof obligations, partner access, and governance requirements are absent, leaving cost bands low-confidence."}},"blocking_evidence":["Evidence that actual sport-science specifications or interfaces admit open-ended executable model classes, universal guarantees, or timeout-derived Boolean verdicts.","A precise formalization of the represented model-controller class, quantifiers, horizon, prohibited-state predicate, and timeout semantics.","Demonstration that fragment membership and scope enforcement cannot be bypassed when models or guarantees change.","A predeclared shadow-test protocol defining library inclusion, reference labels, false-SAFE and ambiguity measures, usable-answer rate, and minimum acceptable utility.","Independent review of any claimed total procedure, impossibility reduction, or conservative abstraction.","Evidence that intended adopters and authorizers consider the resulting evidence labels useful enough to integrate into decisions."],"next_evidence_step":"Conduct a bounded, non-interventional inventory of actual or previously reviewed analyzer specifications and model interfaces, then compare each published or implied safety guarantee with its formally admitted model class, horizon, and timeout behavior. The problem is falsified if every admitted pair is fixed, finite, scope-enforced, and already covered by a total correct procedure with no universal or timeout-as-verdict claim; otherwise use the documented cases to predeclare a finite shadow-test library and decision thresholds.","research_questions":["Do current sport-science analyzer requirements actually quantify over open-ended executable athlete models or controllers?","Are timeouts, failed searches, or finite simulations ever communicated as Boolean safety evidence?","Can the exact verification requirement and prohibited-state predicate be formalized faithfully enough for independent review?","Which practically relevant model fragments have enforceable membership and a total verification procedure?","Does boundary-aware routing reduce false-SAFE labels and guarantee ambiguity relative to the baseline and stated rival?","What usable-answer rate and false-alarm burden will coaches, scientists, and governance reviewers accept?","How often do model or controller changes require reclassification, and can those triggers be reliably audited?","Which elements, if any, are distinctive relative to existing formal-verification and model-governance practice?","Does predictive or bounded statistical evidence meet the real stakeholder need without invoking universal verification?"] ,"recommendation":"VALIDATE_PROBLEM_FIRST","uncertainty_constraints":["Closed-book assessment: no external evidence was used.","Problem prevalence, stakeholder demand, market size, realized impact, and prior art are unmeasured.","The computability issue applies only to the selected executable representation and exact guarantee, not to physical athletes or physiology in general.","No undecidability or impossibility result has been established for the proposed sport-science class.","Formal correctness would not establish that the athlete model or prohibited-state predicate is valid.","Cost bands are resource-equivalent planning ranges, not quotations, and depend heavily on model-language complexity and existing infrastructure.","The intervention may fail through excessive UNKNOWN results, false alarms, unenforceable fragment membership, or marketing that exceeds the checked guarantee."],"closed_book_prior_art_boundary":"Prior-art status is UNSEARCHED. This assessment makes no claim that the composition is novel, uncommon, or superior to existing formal verification, hybrid-systems safety, model-governance, or sport-science validation practice; those comparisons require external research."}