{"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__environmental_climate","archetype_slug":"computability_boundary_mapping","domain_slug":"environmental_climate","title":"Computability-aware review of executable environmental models","opportunity_summary":"Evaluate whether environmental model governance can avoid unsupported universal safety claims by enforcing a decidable model fragment and routing other submissions to explicitly labeled bounded, sound, or UNKNOWN modes. The candidate has a precise failure theory and safe test design, but actual stakeholder demand, the reduction, operational language coverage, usability, and distinctiveness remain unverified.","adopter_authorizer":"An environmental regulator or model-governance owner can authorize classification work and a sandboxed study; only the legally designated authority can alter clearance rules.","scores":{"meaningful_impact":{"score":4,"rationale":"If the stated universal-verification demand exists, preventing false threshold-safety certification and wasted investment could materially protect environmental review quality. The affected decisions may be consequential, although the prevalence of this demand and resulting errors is unsupported."},"stakeholder_pull":{"score":2,"rationale":"The candidate identifies regulators, governance teams, submitters, communities, and ecosystems, but says only that reviewers may require the universal verdict. It provides no affirmative evidence of an adopter requesting the capability or experiencing the described timeout-to-verdict failure."},"incremental_advantage":{"score":4,"rationale":"Relative to budget-limited simulation and heuristic alarms, the proposed fragment enforcement, proof-scoped guarantees, and explicit UNKNOWN routing directly address overclaimed universal assurance. The advantage depends on proof validity, useful fragment coverage, and acceptable abstention rates."},"distinctiveness_plausibility":{"score":3,"rationale":"Applying computability boundaries, enforceable fragments, and guarantee-labeled routing to environmental model governance is a coherent combination, but prior art is explicitly unsearched, so distinctiveness cannot be established closed-book."},"technical_implementability":{"score":3,"rationale":"A parser-enforced fragment and sandboxed comparison on 35 models appear bounded, while the reduction, semantic-fidelity mapping, checker proof, sound abstractions, and operational complexity remain unresolved. Implementation feasibility may differ substantially across submission languages."},"adoption_authority_feasibility":{"score":4,"rationale":"The regulator or governance owner is expressly identified as able to authorize classification and a sandboxed pilot, and live clearance authority is appropriately reserved. Broader adoption would still require legal authorization and workflow agreement."},"evidence_readiness":{"score":3,"rationale":"The candidate supplies a 30-historical-plus-5-adversarial test set design, comparison modes, falsifiers, exclusions, and rollback conditions. However, the reduction is only a hypothesis, historical-case eligibility is undefined, and access to suitable models and independent reviewers is not confirmed."},"safety_net_benefit":{"score":5,"rationale":"Explicit separation of SAFE, violation found, UNKNOWN, out-of-scope, and tool failure directly prevents timeouts or non-findings from becoming false safety claims. The nonbinding sandbox, stop conditions, label withdrawal, and reversion to human review provide a strong pilot safety net."},"scalability":{"score":3,"rationale":"Parser enforcement and standardized labels could be reused within a stable model class, but each language and threshold semantics may require substantial formalization. Fragment exclusion, high UNKNOWN rates, and checker complexity could limit cross-program scaling."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"50K_TO_250K","scope":"A sandboxed study covering 30 previously reviewed models and 5 synthetic adversarial models, one formalized threshold property, a checked reduction, independent review, one prototype parser-enforced fragment, and exact-versus-bounded-versus-UNKNOWN comparison.","confidence":"MODERATE","assumptions":["Historical models and review records can be accessed without procurement-intensive data recovery.","The study addresses one submission language and one threshold-property family.","Costs include formal-methods labor, environmental-model expertise, independent review, secure computing, coordination, and evaluation.","No pilot output affects live clearance decisions."]},"initial_deployment_startup":{"band_2026_usd":"250K_TO_1M","scope":"Production hardening for one regulator or governance program, including language specification, checker and router implementation, security review, audit logging, workflow integration, documentation, training, and acceptance evaluation.","confidence":"LOW","assumptions":["Deployment is limited to one established submission workflow and a small number of model classes.","Existing identity, case-management, and sandbox infrastructure can be integrated rather than replaced.","Legal review permits advisory use while clearance authority remains human-controlled.","Proof and test artifacts from first evidence remain usable."]},"operational_launch":{"band_2026_usd":"1M_TO_5M","scope":"Launch across an operational review program with multiple model classes, validated abstractions, service monitoring, submitter support, reviewer training, governance procedures, and pre-production evaluation.","confidence":"LOW","assumptions":["Several model languages or materially different semantics require separate treatment.","Operational service levels and protected-data controls are required.","Independent validation and change-control processes precede live advisory use.","The decidable fragments cover enough submissions to justify launch."]},"annual_recurring":{"band_2026_usd":"250K_TO_1M","scope":"Annual maintenance for one operational program, including model-language updates, proof and regression maintenance, monitoring, security and compliance, reviewer support, incident handling, and periodic recalibration of UNKNOWN routing.","confidence":"LOW","assumptions":["The supported language set changes gradually rather than being redesigned each year.","A small specialist formal-methods and domain-governance team is retained.","Major new model classes or regulatory expansions are treated as additional startup work.","Infrastructure needs remain moderate and do not require large-scale simulation capacity."]}},"research_burden":"HIGH","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"UNCERTAIN","reason":"The candidate describes an intelligible and consequential failure mode, but supplies no external confirmation that any stakeholder requires a total exact class-wide verdict or currently converts timeouts into clearance decisions."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"The environmental regulator or model-governance owner is identified as the pilot authorizer, with live rule changes reserved to the legally designated authority."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The proposal makes a testable claim that an enforced decidable fragment plus labeled fallback routing will prevent overstated exact verdicts compared with budget-expiring simulation and heuristic review."},"bounded_next_evidence_step":{"status":"YES","reason":"The sealed candidate specifies 30 historical and 5 adversarial models, one threshold property, one enforced fragment, independent proof review, explicit comparison modes, and predeclared falsifiers without live deployment."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"For the proposed evidence step, outputs are nonbinding and sandboxed; permit actions are excluded; overstated labels, proof failure, semantic disagreement, and data escape trigger stopping and rollback."},"implementation_cost_scope_and_range":{"status":"UNCERTAIN","reason":"Broad resource bands can be estimated, but the sealed candidate does not establish language count, model complexity, integration requirements, data controls, staffing, or service levels needed for a defensible implementation range."}},"blocking_evidence":["Confirmation that at least one intended adopter actually requires or is considering a total exact class-wide threshold verdict.","A mechanically documented characterization of the accepted submission language, horizons, semantics, and existing restrictions.","A formal halting-to-threshold construction with checked source and target encodings, preservation argument, and independent review.","Evidence that the parser-enforced fragment preserves intended model semantics and covers a useful share of eligible submissions.","Pilot results showing no false exact labels or SAFE-from-timeout behavior and an UNKNOWN rate at or below the predeclared 60% usability falsifier.","External prior-art research sufficient to assess whether the governance pattern is distinctive or already standard practice."],"next_evidence_step":"With one regulator or model-governance partner, conduct the authorized nonbinding sandbox study on 30 eligible historical models and 5 synthetic adversarial models: formalize one threshold property, independently review the reduction and exact-checker obligations, enforce one decidable fragment, and compare exact, bounded, and UNKNOWN outputs against the existing budget-limited review. Stop or reject the lever if the reduction or proof fails, any timeout becomes SAFE, any exact label is false, or UNKNOWN exceeds 60% of predeclared eligible historical cases.","research_questions":["Does any intended stakeholder require a total exact verdict for every accepted executable model and admissible encoded future?","Are accepted inputs already finite-state, bounded-horizon, or otherwise mechanically restricted to a decidable class?","Can the proposed reduction be fully formalized and independently verified without overextending its conclusion to physical climate prediction?","What fragment can be mechanically enforced while preserving the semantics needed by real environmental submissions?","How often do historical cases receive exact, bounded, UNKNOWN, out-of-scope, or tool-failure labels under predeclared eligibility rules?","What false-positive burden and UNKNOWN rate can reviewers and submitters operationally tolerate?","How do fragment restrictions and formalization costs affect smaller or less-resourced model developers?","What prior systems already combine computability-boundary analysis, enforced fragments, and guarantee-labeled routing in environmental or regulated model review?"],"recommendation":"PARTNERED_RESEARCH","uncertainty_constraints":["Closed-book assessment cannot establish problem prevalence, stakeholder demand, market size, prior art, novelty, or realized impact.","The reduction and exact-checker correctness are hypotheses rather than completed certificates.","The candidate does not establish access to the proposed historical models, independent reviewers, or protected-data infrastructure.","Cost bands are resource-equivalent planning ranges, not quotations, and are highly sensitive to language count, integration scope, and compliance obligations.","The 60% UNKNOWN falsifier is predeclared but not justified as an operationally acceptable threshold.","Verification of an encoded model property would not establish empirical adequacy of the model or predictability of the physical climate system."],"closed_book_prior_art_boundary":"Prior art is explicitly marked UNSEARCHED. This assessment therefore makes no claim about novelty, prevalence, competing implementations, market position, or whether computability-aware environmental model governance already exists."}