{"schema_version":1,"assessment_id":"eoa_inverse_innovation_exp03_opportunity320_20260801","source_experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"invariant_mode_decomposition_design__agricultural_science","archetype_slug":"invariant_mode_decomposition_design","domain_slug":"agricultural_science","title":"Modal targeting for coupled within-field crop stress","opportunity_summary":"Test whether locally estimated soil–crop transition modes reveal persistent, management-responsive stress or nitrogen-loss risks missed by separate sensor thresholds, and whether targeting those modes improves preregistered outcomes versus ordinary threshold management and a direct multivariable predictive rival.","adopter_authorizer":"A farm operator would adopt and authorize management changes, with agronomist and applicable environmental or trial oversight approval.","scores":{"meaningful_impact":{"score":3,"rationale":"If the hypothesized blind spots occur, preventing yield loss, excess irrigation, or nitrogen loss would be meaningful, but the sealed candidate provides no evidence of their prevalence, magnitude, or preventability."},"stakeholder_pull":{"score":2,"rationale":"Farm operators and advisers have plausible incentives to protect yield and reduce inputs and losses, but no expressed demand, workflow dissatisfaction, willingness to participate, or willingness to pay is supplied."},"incremental_advantage":{"score":3,"rationale":"The proposal adds dynamic-mode stability, sensitivity, drift, and residual diagnostics to independent thresholds and makes a comparative claim against a direct multivariable model; whether those interpretations improve decisions rather than merely redescribe predictions is unresolved."},"distinctiveness_plausibility":{"score":3,"rationale":"The combined mode-identification, modal-intervention, and guarded randomization design is specific enough to investigate, but prior art is explicitly unsearched and no originality claim can be supported closed-book."},"technical_implementability":{"score":3,"rationale":"The state variables, interventions, comparisons, and stopping rules are specified, but reliable transition estimation may be undermined by sparse sampling, calibration, nonlinear dynamics, confounding, nonstationarity, poor conditioning, or mode identity changes."},"adoption_authority_feasibility":{"score":4,"rationale":"The farm operator, agronomist, and applicable oversight roles are explicitly identified, and bounded adjustments, rescue treatment, exclusions, stopping criteria, and rollback fit a controlled partner study; actual approvals and partner commitment remain unverified."},"evidence_readiness":{"score":3,"rationale":"The candidate supplies observable variables, falsifiers, held-out checks, two comparators, and preregistered endpoints, but begins at hypothesis maturity and may first require sufficient historical zone-level time-series data before safe intervention testing."},"safety_net_benefit":{"score":4,"rationale":"Used as a guarded supplement, the approach could flag coupled risks missed by independent thresholds, while residual, conditioning, spectral-gap, drift, crop-stress, and nutrient-loss checks provide explicit retirement and rollback triggers; the detection benefit is still hypothetical."},"scalability":{"score":2,"rationale":"Each crop, soil, growth stage, weather regime, and management system may require local identification, sensors, calibration, and renewed validation, while the candidate expressly excludes extrapolation beyond the tested setting."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"10K_TO_50K","scope":"A bounded retrospective feasibility analysis using an existing, sufficiently dense zone-level season dataset to estimate modes and compare held-out explanatory and predictive performance against independent thresholds and a direct multivariable model.","confidence":"LOW","assumptions":["Suitable historical sensor, input, weather, outcome, and nitrate-loss-proxy data already exist and can be accessed.","No new field instrumentation or live management change is included.","The band includes data preparation, agronomic review, modeling, preregistration of analysis, and evaluation labor."]},"initial_deployment_startup":{"band_2026_usd":"50K_TO_250K","scope":"Prepare one partner farm for a controlled zone-level study, including sensor verification or additions, calibration, data integration, protocol design, approvals, staff training, model development, and safety monitoring setup.","confidence":"LOW","assumptions":["The farm already has some precision-management infrastructure.","The study covers one crop and a bounded number of comparable zones.","Major irrigation-system replacement, laboratory platform construction, and land acquisition are excluded."]},"operational_launch":{"band_2026_usd":"50K_TO_250K","scope":"Execute and evaluate one-season blocked randomization of modal-guided, ordinary-threshold, and direct multivariable management under preapproved agronomic limits.","confidence":"LOW","assumptions":["Only small irrigation or nitrogen adjustments are tested.","The band includes field coordination, sampling, input application, data management, oversight, rescue capacity, and comparative analysis.","Weather or crop failure does not require repeating the entire season."]},"annual_recurring":{"band_2026_usd":"50K_TO_250K","scope":"Maintain a single-farm operational program with sensing, calibration, data access, agronomic supervision, repeated model fitting, drift and residual monitoring, reporting, and periodic validation.","confidence":"LOW","assumptions":["Operation remains limited to one farm and an established sensor network.","Local re-estimation is required across seasons or growth stages.","Expansion to additional farms, crops, or major hardware replacement is excluded."]}},"research_burden":"HIGH","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"YES","reason":"The candidate identifies a concrete management failure mode—separate thresholds missing coupled soil–crop states—with observable yield, water-use, crop-stress, and nitrogen-loss consequences, although its prevalence is unestablished."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"The farm operator is identified as the management-change authority, with agronomist and applicable environmental or trial oversight approval."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The proposal can test whether reproducible modes add information beyond independent thresholds and whether modal targeting improves registered outcomes relative to both threshold management and a direct multivariable predictive rival."},"bounded_next_evidence_step":{"status":"YES","reason":"A retrospective held-out analysis of one existing zone-level dataset can safely test mode reproducibility, separation, stability, action sensitivity, residual performance, and incremental value before any live intervention."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The sealed design limits inputs to approved ranges, excludes hazardous extrapolation and unapproved pesticide changes, preserves rescue treatment, names oversight, and specifies multiple halt and rollback conditions."},"implementation_cost_scope_and_range":{"status":"UNCERTAIN","reason":"A one-farm controlled scope can be bounded, but acreage, zone count, existing sensor infrastructure, sampling intensity, data rights, laboratory needs, and oversight requirements are absent, so resource bands remain assumption-sensitive."}},"blocking_evidence":["No evidence establishes that adverse outcomes contain a repeatable coupled residual pattern beyond single-variable thresholds and exogenous shocks.","No evidence shows a sufficiently separated, well-conditioned mode that persists across held-out zones or adjacent sampling windows.","No evidence shows that feasible irrigation or nitrogen adjustments can move the estimated mode reliably and safely.","No comparative evidence shows improved agronomic outcomes over both ordinary threshold management and a direct multivariable predictive model.","No adopter-demand, partner-commitment, data-availability, or site-specific resource evidence is supplied.","Prior art is unsearched, so distinctiveness and freedom to position the method as novel are unresolved."],"next_evidence_step":"Using one existing, access-authorized season of repeated zone-level data, preregister a held-out comparison of independent thresholds, a direct multivariable predictor, and the proposed local modal model. Proceed toward a live pilot only if a coupled mode is reproducible across zones and adjacent windows, exceeds preset conditioning and spectral-gap criteria, adds residual or consequence prediction beyond both comparators, and has feasible simulated management sensitivity; retire the claim if those criteria fail.","research_questions":["Do ordinary-management failures exhibit a repeatable multivariable residual pattern after accounting for single-variable thresholds and exogenous weather shocks?","Are estimated modes stable, sufficiently separated, and robust to sensor scaling, calibration uncertainty, sampling windows, and held-out zones?","Can feasible irrigation or nitrogen actions move the targeted mode without harmful registered coupling or violations of agronomic limits?","Does modal targeting improve yield, water use, crop-stress, or nitrogen-loss proxies versus both threshold management and a direct multivariable predictive rival?","How often do non-normal transient growth, mode swaps, or developmental drift invalidate the eigenvalue-based stability classification?","Will farm operators and agronomists accept the added sensing, analysis, oversight, and decision complexity?","What prior work already combines agronomic state-space identification, dynamic modes, actionable sensitivity, and guarded field randomization?","What site-specific infrastructure and coordination determine the actual startup and recurring resource bands?"],"recommendation":"PARTNERED_RESEARCH","uncertainty_constraints":["Closed-book assessment provides no evidence about problem prevalence, market size, realized effect size, or willingness to adopt.","Prior-art status is explicitly UNSEARCHED; distinctiveness is not established.","Evidence maturity is HYPOTHESIS, and the existence and management relevance of stable local modes are unverified.","Agricultural dynamics may be nonlinear, partially observed, weather-driven, non-normal, and nonstationary, limiting invariant-mode interpretation.","Cost bands depend strongly on unavailable details about acreage, sensors, data quality, sampling, laboratory proxies, and oversight.","Results from one crop, soil, stage, season, or weather regime cannot support extrapolation under the candidate's own limits."],"closed_book_prior_art_boundary":"No external search was performed. The sealed packet states that prior art is unsearched and supports neither novelty nor prevalence claims. Any distinctiveness assessment is therefore limited to the internal specificity and testability of the proposed combination, pending comparison with agronomic state-space control, precision agriculture, crop-system identification, and reduced-order modeling work."}