{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"invariant_mode_decomposition_design__agricultural_science","trajectory_id":"R","attempt_index":0,"candidate_sha256":"2b1c22f5f7078f454fea7a58ea31cc5bb5e9c9bf6a198eb899439bdad8064eea","gates":{"G1":{"status":"PASS","reason":"The agronomic problem is independently recognizable: separate management thresholds can miss persistent coupled soil–crop states with yield, resource-use, and nutrient-loss consequences."},"G2":{"status":"PASS","reason":"The proposal explicitly maps a bounded transition operator, state vector, invariant directions, modal gains, selection rules, intervention coordinates, residual checks, drift monitoring, and interpretation limits into the agricultural setting."},"G3":{"status":"PASS","reason":"The causal chain links repeated measurements to a local transition map, mode identification, feasible management perturbation, targeted mode displacement, and comparative agronomic outcomes. Randomization against both ordinary management and a direct multivariable rival can distinguish modal leverage from prediction alone."},"G4":{"status":"PASS","reason":"Every archetype component has a domain realization, and the selected mechanisms retain their proper roles. Eigenanalysis supplies dynamic modes, sensitivity connects them to action, residual testing checks omitted behavior, and conditioning, coupling, gap, and drift diagnostics constrain use."},"G5":{"status":"PASS","reason":"Effectiveness and mode existence are consistently bounded as hypotheses or inferences. The candidate makes no empirical effectiveness or novelty claim, identifies confounding and scaling threats, and specifies held-out and randomized tests rather than presenting conjecture as evidence."},"G6":{"status":"PASS","reason":"The problem falsifier tests whether coupled modal structure adds explanatory content beyond thresholds and exogenous shocks, while the intervention falsifier separately tests whether moving an estimated mode improves outcomes relative to the baseline and nearest rival."},"G7":{"status":"PASS","reason":"Decision authority, oversight, affected parties, bounded pilot actions, excluded actions, rescue treatment, stopping conditions, and rollback to approved management are explicit and proportionate to the proposed field test."}},"scores":{"structural_fit":{"score":4,"reason":"The transfer preserves the archetype's full transformation-to-mode-to-action structure, including residual visibility, local validity, coupling, spectral separation, and retirement rules."},"domain_fidelity":{"score":3,"reason":"The state variables, management controls, field-zone design, agronomic limits, crop rescue obligations, and nutrient-loss concerns are credible, although actual observability and sampling adequacy remain site-dependent."},"causal_plausibility":{"score":3,"reason":"A bounded randomized perturbation can test whether feasible inputs move a reproducible mode and improve outcomes, but nonlinear, partially observed, weather-driven dynamics may prevent reliable operator identification or independent modal control."},"component_translation":{"score":4,"reason":"All named components are translated into operational agricultural realizations, and mechanism selection distinguishes dynamic eigenmodes from covariance summaries, centrality, and inappropriate physical excitation protocols."},"adversarial_survival":{"score":4,"reason":"The candidate directly addresses nonstationarity, non-normal transient amplification, scaling artifacts, sparse sampling, mode swapping, confounding, weak separation, nonlinear residuals, and failure against a strong predictive rival."},"reframing_gain":{"score":4,"reason":"The proposal changes the decision unit from isolated sensor thresholds to coupled state directions with persistence and controllability, yielding a materially different early-warning and management hypothesis."},"practicality_testability":{"score":3,"reason":"The blocked field comparison, predeclared endpoints, bounded treatments, held-out validation, stopping rules, and rival comparison are executable, though data density and stable local estimation may be demanding within a growing season."},"expected_value_risk":{"score":3,"reason":"A small reversible pilot could reveal useful coupled management leverage while limiting crop and environmental exposure, but mistargeted water or nitrogen remains consequential and requires the stated safeguards."},"novelty_evidence":{"score":0,"reason":"Prior art is explicitly unsearched, so no evidence supports a novelty judgment."}},"weighted_total":83.75,"disposition":"DEEP_RESEARCH","fabrication_findings":[],"weak_dimensions":["novelty_evidence"],"actionable_critique":[{"priority":"MEDIUM","issue":"The proposal has no prior-art evidence for novelty.","repair":"Before making any novelty claim, compare the combined dynamic-mode identification, modal intervention, and guarded field-randomization design with agronomic state-space control, precision agriculture, crop-system identification, and reduced-order modeling literature.","evidence_boundary":"This closed-book evaluation supports structural quality and testability only; it does not establish originality or empirical effectiveness."}],"repairs":[],"improvement_attribution":{"kind":"NONE","reason":"This is an original attempt with no prior repair history, and neither the problem identifier nor the causal-lever identifier changed."},"trajectory_replacement":false,"arm_guess":"MECHANISM_PACKET","recommendation":"SUCCESS","tester_summary":"The candidate is a structurally complete, domain-grounded, falsifiable transfer with a credible causal test and strong safety boundaries. Its principal unresolved weakness is the absence of novelty evidence, which does not defeat the explicitly conjectural and test-oriented proposal."}