{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"layer_decay_and_expiration_management__agricultural_science","trajectory_id":"R","attempt_index":0,"candidate_sha256":"3eff554eecc27e001602a0640ee3ee293cd1a6265c261eac2a49da85d212ee9f","gates":{"G1":{"status":"PASS","reason":"The agricultural problem stands independently: obsolete decision layers can mislead users, while indiscriminate removal can damage lineage, evidence, and longitudinal research."},"G2":{"status":"PASS","reason":"Sequential field-data deposits, active staleness, retention tension, lifecycle states, dependencies, archival paths, and deletion records correspond closely to the archetype."},"G3":{"status":"PASS","reason":"Inventory, validity labeling, filtered views, retention authority, dependency gates, supersession markers, and recovery drills directly target stale selection and unsafe cleanup."},"G4":{"status":"PASS","reason":"Every archetype component has a domain realization, load-bearing mechanisms retain their defining machinery, and unsuitable clock- or sequence-blind mechanisms are explicitly rejected."},"G5":{"status":"PASS","reason":"The candidate distinguishes inference from hypothesis, declares prior art unsearched, avoids prevalence or effect-size claims, and confines empirical claims to a bounded test."},"G6":{"status":"PASS","reason":"The problem falsifier tests whether stale-layer confusion or burden exists, while the intervention falsifier separately tests whether lifecycle controls improve tasks without unacceptable failures."},"G7":{"status":"PASS","reason":"Authority is divided among data stewards, agronomic owners, compliance officers, and custodians; the pilot is sandboxed, non-operational, reversible, and protected by explicit halt conditions."}},"scores":{"structural_fit":{"score":4,"reason":"The proposal instantiates the full lifecycle-governance structure, including differentiated disposition, dependency protection, exception handling, rollback, and bounded active access."},"domain_fidelity":{"score":4,"reason":"The translation uses agronomically meaningful objects and context signals, including seasons, crop and boundary changes, calibration, units, prescriptions, spatial formats, and research lineage."},"causal_plausibility":{"score":3,"reason":"The causal sequence is coherent and identifies load-bearing controls, but the prevalence of stale selection and the magnitude of workflow improvement remain empirical hypotheses."},"component_translation":{"score":4,"reason":"The component map is complete, specific, and preserves distinctions between current-decision authority, historical scientific value, archival recoverability, and destruction authority."},"adversarial_survival":{"score":4,"reason":"The candidate addresses old layers that gain scientific value, interacting layer sets, hidden dependencies, false precision, failed restores, privacy conflicts, and time-sensitive retrieval."},"reframing_gain":{"score":3,"reason":"It reframes data accumulation from a storage problem into governance of decision authority, scientific memory, lineage, and reversible disposition."},"practicality_testability":{"score":3,"reason":"A consenting-farm sandbox pilot, comparison tasks, observable failures, rollback, and source preservation make testing feasible, though sampling and tolerance definitions still need protocol-level detail."},"expected_value_risk":{"score":4,"reason":"The authorized first step can reveal workflow value with limited downside because it neither deletes sources nor changes prescriptions or field operations."},"novelty_evidence":{"score":0,"reason":"The candidate explicitly reports unsearched prior-art status and supplies no evidence that the proposed synthesis is novel in agricultural data governance."}},"weighted_total":86.25,"disposition":"DEEP_RESEARCH","fabrication_findings":[],"weak_dimensions":["novelty_evidence"],"actionable_critique":[{"priority":"LOW","issue":"Novelty and prior-art status are unresolved.","repair":"During deep research, compare the proposal with existing precision-agriculture catalog, geospatial lineage, records-retention, and archival-governance systems before making novelty claims.","evidence_boundary":"This is an external evidence gap, not a defect in the closed-book structural or causal mapping."},{"priority":"LOW","issue":"The bounded test does not yet define its sampling frame or acceptable error and recovery tolerances.","repair":"Predeclare representative layer classes, user tasks, comparison conditions, false-deactivation limits, lineage checks, and restore-success criteria in the pilot protocol.","evidence_boundary":"The candidate already identifies the relevant outcomes and failure modes; numerical thresholds require local operational evidence."}],"repairs":[],"improvement_attribution":{"kind":"NONE","reason":"This is an original attempt with unchanged problem and causal-lever identifiers; the remaining uncertainty is evidentiary rather than attributable to a prior conceptual or operational revision."},"trajectory_replacement":false,"arm_guess":"MECHANISM_PACKET","recommendation":"SUCCESS","tester_summary":"The candidate is an unusually complete and disciplined transfer: it preserves the archetype's lifecycle logic, translates it credibly into precision agriculture, separates decision validity from historical value, and proposes a reversible test. Deep research should establish prior art and calibrate the pilot protocol."}