{"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,"archetype_slug":"invariant_mode_decomposition_design","domain_slug":"agricultural_science","decision":"CANDIDATE","problem_id":"coupled_crop_stress_mode_blindness","causal_lever_id":"management_response_to_weakly_damped_field_modes","proposal":{"problem":"Within-field irrigation and nitrogen management based on separate sensor thresholds can miss coupled soil–crop states that persist or amplify across management cycles, allowing crop stress or nitrogen-loss risk to develop despite acceptable field averages. Whether such locally stable modes exist and improve decisions is a HYPOTHESIS.","actors_substrate":["farm manager and crop adviser","workers applying irrigation or fertilizer","crop plants and soil biota","soil-water-nitrogen-canopy state within management zones","downstream water users and ecosystems"],"observable_state":"Repeated zone-level vectors of root-zone moisture, mineral nitrogen, canopy temperature or greenness, pest pressure, recent weather, irrigation, fertilizer input, yield response, and nitrate-loss proxy.","consequence":"Threshold decisions may repeatedly under-treat or over-treat zones whose risk lies in a multivariable direction, producing preventable yield loss, water waste, or nitrogen loss.","affected_objective":"Maintain yield and crop health while limiting irrigation, fertilizer use, and off-field nitrogen loss.","structural_mapping":[{"archetype_element":"Coupled transformation","domain_realization":"A locally fitted transition map from one management-cycle soil–crop state to the next, conditional on weather and recorded inputs.","claim_kind":"HYPOTHESIS"},{"archetype_element":"Invariant directions and scalar responses","domain_realization":"Eigenmodes of the fitted transition map represent recurring combinations of soil moisture, nitrogen, canopy condition, and pest pressure; gains estimate decay, persistence, or growth.","claim_kind":"INFERENCE"},{"archetype_element":"Action-relevant modes","domain_realization":"Modes are ranked by predicted and experimentally observed effects on yield, water use, and nitrogen-loss proxies rather than by magnitude alone.","claim_kind":"HYPOTHESIS"},{"archetype_element":"Residual and regime limits","domain_realization":"Out-of-sample reconstruction error, nonlinear residual structure, weather regime, crop stage, and management range bound use of the modal model.","claim_kind":"INFERENCE"},{"archetype_element":"Drift governance","domain_realization":"Mode rotation, gain changes, and spectral-gap loss trigger review or retirement after crop-stage, weather, soil, or practice shifts.","claim_kind":"HYPOTHESIS"}],"component_map":[{"component":"Transformation Scope","status":"adapted","domain_realization":"One-step zone-state transition within a specified crop stage, weather band, soil class, and input range."},{"component":"State-Vector Definition","status":"direct","domain_realization":"Standardized soil moisture, mineral nitrogen, canopy, pest, weather, input, and outcome measurements with units and sampling cadence fixed."},{"component":"Invariant Mode Basis","status":"direct","domain_realization":"Eigenvectors of the locally estimated transition operator, traced back to measured variables."},{"component":"Modal Gain Spectrum","status":"direct","domain_realization":"Eigenvalues with uncertainty intervals for estimated persistence, decay, oscillation, or growth."},{"component":"Dominant Mode Selection Rule","status":"adapted","domain_realization":"Retain modes exceeding predeclared outcome-sensitivity and validation thresholds, not variance alone."},{"component":"Stable/Unstable Mode Partition","status":"direct","domain_realization":"Classify modes by discrete-time gain relative to unity, with an uncertainty category around the boundary."},{"component":"Modal Intervention Map","status":"adapted","domain_realization":"Map feasible irrigation and nitrogen changes to modal displacement and agronomic outcomes."},{"component":"Reconstruction Residual Check","status":"direct","domain_realization":"Out-of-sample state and consequence reconstruction error plus inspection for structured residuals."},{"component":"Mode Drift Monitor","status":"direct","domain_realization":"Scheduled comparison of mode direction, gain, ordering, and residuals."},{"component":"Interpretation Scope Contract","status":"direct","domain_realization":"Modes are local predictive constructs, not automatically biological causes, and apply only inside recorded conditions."},{"component":"Mode-Coupling Register","status":"direct","domain_realization":"Record near-degenerate, non-orthogonal, nonlinear, and intervention-induced cross-effects."},{"component":"Local Linearization Window","status":"direct","domain_realization":"Explicit bounds for crop stage, antecedent moisture, temperature, rainfall, and input size."},{"component":"Spectral Gap Threshold","status":"direct","domain_realization":"Predeclared minimum separation and mode-angle stability required for retaining a reduced basis."}],"mechanism_dispositions":[{"slug":"eigendecomposition_workflow","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Decompose the explicitly fitted local transition matrix; report conditioning and uncertainty.","counterfactual_removal":"Without modes and gains, the proposed coupled-direction diagnosis cannot be made."},{"slug":"modal_sensitivity_sweep","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Perturb feasible input coordinates and modal states within agronomic bounds to rank outcome leverage and cross-effects.","counterfactual_removal":"Removal leaves no defensible link from descriptive modes to management action."},{"slug":"modal_stability_analysis","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Classify locally growing, decaying, marginal, and oscillatory soil–crop modes with uncertainty.","counterfactual_removal":"Removal eliminates the weak-damping or amplification claim central to early warning."},{"slug":"mode_shape_testing","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Field observations and bounded management perturbations validate the fitted transition model, but vibration-style excitation and response functions are not an appropriate identification protocol.","counterfactual_removal":"No material change; validation remains supplied by randomized field perturbations and residual tests."},{"slug":"network_spectral_centrality_analysis","disposition":"incompatible","contribution_type":"NONE","adaptation_or_rejection":"The target state is a dynamical soil–crop vector, not a node-importance problem; centrality would answer a different question.","counterfactual_removal":"No change to the causal chain."},{"slug":"power_iteration_probe","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"A bounded pilot has a modest state dimension and needs multiple modes and uncertainty; dominant-only iteration is unnecessary.","counterfactual_removal":"No change because full decomposition supplies the selection evidence."},{"slug":"principal_component_analysis","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Variance directions may summarize measurements but do not establish transition persistence or management leverage.","counterfactual_removal":"No change; rejecting it prevents covariance modes from being mistaken for dynamic modes."},{"slug":"reduced_order_model","disposition":"selected_supporting","contribution_type":"OPERATIONAL","adaptation_or_rejection":"Use retained modes as a small, explicitly local simulation for choosing bounded pilot treatments.","counterfactual_removal":"The diagnosis remains, but prospective treatment screening becomes slower and less operational."},{"slug":"residual_reconstruction_test","disposition":"selected_load_bearing","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Evaluate held-out state reconstruction and consequence-weighted residuals across retained mode counts.","counterfactual_removal":"There is no hard check that omitted dynamics are tolerable, making intervention unsafe."},{"slug":"singular_value_decomposition","disposition":"selected_supporting","contribution_type":"TEST_DESIGN","adaptation_or_rejection":"Use singular values and condition diagnostics to detect non-normality or ill-conditioned eigenvectors, not as substitutes for invariant dynamic modes.","counterfactual_removal":"The core analysis remains, but transient amplification and numerical fragility are easier to miss."},{"slug":"spectral_decomposition_report","disposition":"selected_supporting","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Document variable loadings, uncertainty, couplings, residuals, and prohibited causal interpretations for decision makers.","counterfactual_removal":"Technical results remain but misuse outside their scope becomes materially more likely."},{"slug":"spectral_gap_monitor","disposition":"selected_load_bearing","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Re-estimate gaps, mode angles, gains, and residuals at crop-stage or regime changes.","counterfactual_removal":"A once-valid reduced basis could silently persist after losing separation or identity."}],"causal_chain":["Repeated measurements and recorded inputs identify a bounded local soil–crop transition map.","Decomposition exposes coupled directions and gains hidden by separate thresholds.","Stability and sensitivity analyses identify a persistent or growing mode that feasible management can move.","A randomized, bounded treatment shifts that mode while held-out residual and consequence metrics test the prediction.","If outcomes improve without safety-bound violations, modal targeting supports later controlled evaluation; drift or residual failure retires it."],"baseline":"Ordinary zone management applies independent moisture, nitrogen, canopy, or pest thresholds, supplemented by field averages and adviser judgment.","nearest_rival":"A multivariable predictive model directly optimizing yield and nitrogen-loss risk without invariant-mode interpretation or stability classification.","authority_safety":{"affected_parties":["farm owner or operator","farm workers","crop advisers","downstream water users","neighboring ecosystems"],"decision_authority":"The farm operator authorizes management changes; the agronomist and applicable environmental or trial oversight approve protocol and limits.","authorized_first_step":"Run one-season blocked randomization across comparable zones: modal-guided versus ordinary threshold management, using only small preapproved irrigation or nitrogen adjustments; preregister yield, water, nitrogen-loss proxy, mode displacement, and residual endpoints.","excluded_actions":["unapproved pesticide changes","fertilizer or irrigation outside agronomic and legal limits","withholding rescue treatment from visibly stressed crops","extrapolation to other soils, crops, stages, or extreme weather","whole-farm deployment before pilot review"],"halt_rollback":"Stop modal-guided changes and return zones to the approved baseline if crop-stress limits, nutrient-loss limits, eigenvector conditioning, spectral-gap threshold, mode drift, or held-out residual tolerance fails; provide standard rescue treatment where needed."}},"negative_tests":{"strongest_counterevidence":"Independent-variable or black-box multivariable controls may match or outperform modal control because agricultural dynamics are nonlinear, weather-driven, partially observed, and nonstationary; estimated eigenmodes may be artifacts of scaling, confounding, or sparse sampling.","analogy_break":"Unlike a fixed engineered operator, the soil–crop transition changes with development, weather, microbial activity, and management; eigenmodes need not remain invariant long enough to guide action, and non-normal dynamics can produce transient growth not captured by eigenvalue stability.","failure_condition":"No reproducible, sufficiently separated mode remains stable across held-out zones or adjacent sampling windows, or feasible inputs cannot move it independently enough to matter.","problem_falsifier":"Under the ordinary baseline, adverse yield or nitrogen-loss outcomes are fully explained by single-variable thresholds or exogenous shocks, with no repeatable multivariable residual pattern or persistent coupled transition direction.","intervention_falsifier":"A preregistered modal-guided treatment shifts the targeted estimated mode but does not improve yield, water use, or nitrogen-loss proxies relative to baseline and the nearest rival, or produces offsetting harm through registered coupling.","risks":["Overfitting short field histories","Treating predictive modes as biological causes","Unsafe input changes from extrapolation","Missing transient amplification in a non-normal operator","Sensor scaling or calibration artifacts","Environmental harm from mistargeted nitrogen or irrigation","Mode identity swaps when the spectral gap narrows"]},"null_rationale":null,"classification":{"candidate_kind":"TESTABLE_CONJECTURE","prior_art_status":"UNSEARCHED","evidence_maturity":"HYPOTHESIS"},"revision_change_log":{"revision_kind":"ORIGINAL","prior_problem_id":null,"prior_causal_lever_id":null,"problem_changed":false,"causal_lever_changed":false,"conceptual_changes":[],"operational_changes":[],"repairs_addressed":[]},"confidence":0.74,"generator_notes":"Closed-book structural transfer. The candidate is conditional on identifying a locally valid transition operator; no empirical effectiveness or novelty claim is made."}