{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp07_retrospective_selector60_20260803","cell_code":"E7C040","selector_replication":3,"assessments":[{"blind_id":"CANDIDATE_A","problem_reality_importance":89,"causal_archetype_fit":91,"distinctiveness_prior_art_resilience":76,"operational_specificity":92,"falsifiability_test_quality":88,"adopter_partner_path":87,"deployability_complexity":82,"authority_safety_reversibility":94,"strict_potential":84,"empirical_partner_potential":89,"scrutiny_priority":84,"biggest_visible_risk":"The abstract state may omit legally or scientifically material native-record context, making behavioral conformance falsely equate unlike evidence histories.","rationale":"The proposal identifies a consequential migration and interoperability failure and makes the abstract-state-machine archetype causally central through explicit custody, seal, derivation, quantity, correction, authorization, and error semantics. Its synthetic adapter comparison is bounded, reversible, and capable of exposing contract omissions. Scrutiny should focus on whether a sufficiently complete interface can remain representation-independent without excluding signatures, instrument metadata, jurisdiction-specific audit details, or other review-critical artifacts."},{"blind_id":"CANDIDATE_B","problem_reality_importance":85,"causal_archetype_fit":88,"distinctiveness_prior_art_resilience":70,"operational_specificity":90,"falsifiability_test_quality":87,"adopter_partner_path":88,"deployability_complexity":78,"authority_safety_reversibility":93,"strict_potential":78,"empirical_partner_potential":88,"scrutiny_priority":79,"biggest_visible_risk":"Study-relevant event identity and adequate-observation rules may be inseparable from source-specific definitions and collection processes, making the abstraction unstable or misleading.","rationale":"The three-way distinction among recorded event, adequately observed absence, and unknown addresses a real analytical hazard, and the as-of, correction, equivalence, and policy-version rules provide a coherent behavioral contract. The synthetic dual-representation test is safe and discriminating. However, the core semantic choices are heavily study- and source-dependent, resemble disciplined research-data modeling, and could conceal material uncertainty if generalized beyond a tightly bounded study."},{"blind_id":"CANDIDATE_C","problem_reality_importance":88,"causal_archetype_fit":95,"distinctiveness_prior_art_resilience":85,"operational_specificity":93,"falsifiability_test_quality":92,"adopter_partner_path":90,"deployability_complexity":84,"authority_safety_reversibility":95,"strict_potential":90,"empirical_partner_potential":92,"scrutiny_priority":91,"biggest_visible_risk":"A passing software-boundary test may create false confidence because participants can identify exercises through physical, scheduling, factual, or human cues outside the declared observable surface.","rationale":"Opacity and representation-leakage testing are indispensable rather than decorative here: the intervention must conceal a classification while preserving role-specific lifecycle behavior. Submission locking, disclosure, adjudication, correction, and rejected-operation semantics are precise, and the paired ordinary-case/exercise transcript test can directly falsify the bounded claim without involving personnel or live workflows. The remaining uncertainty—whether the observable boundary captures the cues that matter—is well suited to authorized partner scrutiny."},{"blind_id":"CANDIDATE_D","problem_reality_importance":90,"causal_archetype_fit":92,"distinctiveness_prior_art_resilience":78,"operational_specificity":92,"falsifiability_test_quality":89,"adopter_partner_path":89,"deployability_complexity":80,"authority_safety_reversibility":94,"strict_potential":86,"empirical_partner_potential":91,"scrutiny_priority":87,"biggest_visible_risk":"The corpus boundary may hide parser- or method-specific context needed to judge reliability, allowing software conformance to be mistaken for extraction equivalence.","rationale":"The fixed-source-image setting supplies a strong comparator, while sealed snapshots, provenance, qualifications, content commitments, query laws, and explicit errors form a specific and executable contract. Synthetic images make the first study safe, bounded, and capable of changing an adoption decision. The proposal carefully disclaims scientific equivalence, but practical substitutability may still depend on tool-native diagnostics and container context that the opaque interface does not preserve."}],"rank_order":["CANDIDATE_C","CANDIDATE_D","CANDIDATE_A","CANDIDATE_B"],"top_choice":"CANDIDATE_C","portfolio_observation":"All four are unusually complete abstract-state-machine proposals with safe synthetic first studies. C stands out because opacity and leakage resistance are intrinsic to the operational objective; D and A are also strong but face harder boundary-completeness questions, while B depends most heavily on study-specific semantic policy and is most vulnerable to resembling standard disciplined data modeling.","confidence":"HIGH"}