{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp07_retrospective_selector60_20260803","cell_code":"E7C027","selector_replication":3,"assessments":[{"blind_id":"CANDIDATE_A","problem_reality_importance":84,"causal_archetype_fit":88,"distinctiveness_prior_art_resilience":66,"operational_specificity":94,"falsifiability_test_quality":94,"adopter_partner_path":86,"deployability_complexity":65,"authority_safety_reversibility":96,"strict_potential":76,"empirical_partner_potential":90,"scrutiny_priority":84,"biggest_visible_risk":"The campaign model and standardized outputs may suppress unfamiliar but important characterization features before residual scoring, making the model the gatekeeper of evidence against itself.","rationale":"The proposal addresses a plausible scarcity of expert review and follow-up capacity with a bounded, causally coherent residual-routing architecture. Its shadow comparison, protected cases, independent full-package audits, explicit actions, and decompression rules make the decisive uncertainty highly testable. The main strict-lane weakness is that active learning, anomaly detection, and model-guided experiment review are close rivals, while implementation requires substantial standardization and model-governance overhead."},{"blind_id":"CANDIDATE_B","problem_reality_importance":93,"causal_archetype_fit":95,"distinctiveness_prior_art_resilience":36,"operational_specificity":96,"falsifiability_test_quality":98,"adopter_partner_path":94,"deployability_complexity":79,"authority_safety_reversibility":96,"strict_potential":57,"empirical_partner_potential":95,"scrutiny_priority":81,"biggest_visible_risk":"Learning a high-fidelity-minus-baseline correction with uncertainty-governed reference queries looks very close to standard delta-learning and active-learning practice, so little contrastive claim may remain after scrutiny.","rationale":"The computational bottleneck is consequential, the residual is mathematically load-bearing, and the proposed offline study can directly compare the corrected evaluator with both the baseline and an equal-data direct surrogate. Complete reference outputs, invariant checks, independent audits, and immediate fallback provide unusually strong empirical discipline. Its major liability is prior-art vulnerability: the central baseline-plus-learned-correction pattern is highly recognizable despite the added governance layer."},{"blind_id":"CANDIDATE_C","problem_reality_importance":85,"causal_archetype_fit":97,"distinctiveness_prior_art_resilience":78,"operational_specificity":96,"falsifiability_test_quality":97,"adopter_partner_path":89,"deployability_complexity":59,"authority_safety_reversibility":95,"strict_potential":85,"empirical_partner_potential":94,"scrutiny_priority":90,"biggest_visible_risk":"A real external interaction synchronized with recoater motion may resemble the predicted self-generated response and be cancelled while all model-confidence and synchronization checks remain nominal.","rationale":"This is the strongest causal use of the archetype: the outgoing motion command is essential to separating self-generated sensor response from unexplained interaction, rather than serving as decorative context. The bounded shadow pilot includes controlled disturbances, timing and version faults, blinded decision comparisons, raw audits, protected bypasses, and explicit rejection criteria. Real-time multimodal synchronization and validation create deployment complexity, but a willing machine or process partner could resolve the central over-cancellation uncertainty safely and decisively."},{"blind_id":"CANDIDATE_D","problem_reality_importance":57,"causal_archetype_fit":89,"distinctiveness_prior_art_resilience":52,"operational_specificity":93,"falsifiability_test_quality":94,"adopter_partner_path":80,"deployability_complexity":75,"authority_safety_reversibility":96,"strict_potential":52,"empirical_partner_potential":83,"scrutiny_priority":69,"biggest_visible_risk":"The claimed transmission, storage, and review bottleneck may not be binding because Raman spectra are readily stored and compressed and chemists need not inspect every full acquisition manually.","rationale":"The predictive codec is specific, reconstructive, reversible, and backed by a clean shadow-mode test with full raw retention and independent safety channels. However, the proposal depends on a comparatively weakly established capacity problem, and ordinary compression, chemometric monitoring, adaptive acquisition, or anomaly alerting may capture much of the benefit with less synchronization and governance overhead. A partner could test it cheaply, but scrutiny is less valuable until the bottleneck itself appears credible."}],"rank_order":["CANDIDATE_C","CANDIDATE_A","CANDIDATE_B","CANDIDATE_D"],"top_choice":"CANDIDATE_C","portfolio_observation":"C and A offer the best balance of causally essential residual architectures and decision-changing partner studies. B has the strongest computational problem and cleanest benchmark but is unusually exposed to recognizable delta-learning prior art. D is safe and testable yet rests on the least convincing binding constraint.","confidence":"HIGH"}