{
  "version": "parts-inspired-conditional-1.0",
  "protocol": {
    "version": "parts-inspired-conditional-1.0",
    "registered_client_date": "2026-10-04",
    "seed": 20261004,
    "support_cohort": "retrospective",
    "indices": [
      "ndre",
      "ndvi"
    ],
    "tau_days": [
      0,
      7,
      30,
      365
    ],
    "spatial_range_m": [
      0,
      150,
      600
    ],
    "common_fraction": [
      0,
      0.25
    ],
    "spatial_nugget": 0.25,
    "partitions": 4,
    "partition_seeds": [
      20261004,
      20261005,
      20261006
    ],
    "primary": {
      "tau_days": 30,
      "range_m": 150,
      "common_fraction": 0.25
    },
    "simulation_sentinels": [
      {
        "tau_days": 0,
        "range_m": 0,
        "common_fraction": 0
      },
      {
        "tau_days": 30,
        "range_m": 150,
        "common_fraction": 0.25
      },
      {
        "tau_days": 365,
        "range_m": 600,
        "common_fraction": 0.25
      }
    ],
    "simulation_replicates": 500,
    "confidence_level": 0.95,
    "time_origin": "2023-07-01",
    "days_per_year": 365.25,
    "coordinate_crs": "EPSG:32616, WGS 84 / UTM zone 16N; Euclidean distances in metres",
    "mean_model": "Each cell: intercept + elapsed years + centered July day / 30; same observed support for both indices",
    "main_estimand": "Unweighted arithmetic mean of fitted cell-specific temporal slopes on the frozen 176-cell local frame",
    "registration": "Grid and primary scenario fixed before fitting this new experiment. Existing 2021-2025 data have been previously examined. This is retrospective exploratory inference, not preregistration before observing the underlying data.",
    "notes": [
      "No original raster, table, prior output, or existing website file is modified.",
      "Use actual acquisition-day distances, with missing observations omitted and never interpolated.",
      "Temporal R=exp(-abs(day_i-day_j)/tau), tau=0 means independent distinct dates; this is a positive-correlation CAR(1) working model, not native remotePARTS discrete AR(1).",
      "Spatial S=c*11' + (1-c)*[(1-nugget)*exp(-distance/range)+nugget*I]; range=0 means local I. Common c represents a persistent spatially shared acquisition component with the same temporal kernel.",
      "Covariance parameters are fixed sensitivity assumptions, not fitted or optimized for significance. Residual GLS variance scales are estimated cell by cell with n-p divisor.",
      "Two-stage slope covariance is propagated through cell-specific actual-gap GLS influence vectors, including different missingness patterns.",
      "Equal-cell arithmetic mean is the main finite-frame estimand. Full GLS and mean-of-partition GLS implicitly reweight heterogeneous slopes and are secondary common-mean working-model estimators.",
      "Use actual cross-partition covariance K. Diagonal-K standard errors are an explicitly incorrect independence comparator when cross covariance is nonzero.",
      "The independence-equivalent cell count is n*trace(Omega)/(1'Omega1): a variance-equivalent ratio under average marginal slope variance, never a count of independent cells or inferential degrees of freedom.",
      "Wilson Monte Carlo intervals quantify finite-simulation uncertainty in the reported coverage proportions; they are not intervals for real forest accuracy.",
      "All 95% intervals are plug-in Gaussian working-model intervals. Coverage is not guaranteed for real forest observations; no p-values or damage probabilities are emitted.",
      "Null simulations assess conditional Gaussian covariance mechanisms and variance-scale estimation; they do not validate covariance assumptions, the trend model, disturbance accuracy, mask selection, or ecological labels.",
      "Five annual occasions, same-month bursts, inherited CHM and full-period support selection limit temporal and causal interpretation. Nominal cell count is not independent sample size.",
      "Primary anomaly screening and review candidates are preserved. This experiment does not retune the detector."
    ]
  },
  "protocol_sha256": "5debfd356920933316cc2ba0219fb28eab7d45b023b265aeb2bb4f3c43665be6",
  "code_sha256": "f0c91db70406707aa4abb9aff46a8f86bec42184c1b13129ab5d3c3e7ac2bcfa",
  "inputs": {
    "observations": {
      "path": "C:\\badgr\\coordinate_forest_ml_webapp\\runtime\\observation_audit_v1\\cell_date_observations.csv",
      "sha256": "1a509ecd85a1d8810d5ef35d55e1760cc0b63a627ecefea1ed843b8ee55958de"
    },
    "cells": {
      "path": "C:\\badgr\\coordinate_forest_ml_webapp\\runtime\\pilot_v4\\tables\\cell_scores.csv",
      "sha256": "2fb18f884be7bbe80f1ad90ee1abe24c81ef2523061dd0723ea6ed7ddb8aa65e"
    }
  },
  "inputs_unchanged": true,
  "environment": {
    "python": "3.12.13",
    "numpy": "2.4.6",
    "pandas": "3.0.3",
    "scipy": "1.17.1",
    "platform": "Windows-11-10.0.26200-SP0"
  },
  "data": {
    "cells": 176,
    "usable_cell_dates": 3480,
    "usable_dates": 21,
    "dates": [
      "2021-07-03",
      "2021-07-10",
      "2021-07-11",
      "2021-07-25",
      "2021-07-26",
      "2022-07-09",
      "2022-07-10",
      "2022-07-16",
      "2022-07-22",
      "2022-07-23",
      "2023-07-07",
      "2023-07-11",
      "2023-07-22",
      "2023-07-24",
      "2023-07-29",
      "2023-07-31",
      "2024-07-01",
      "2024-07-14",
      "2024-07-31",
      "2025-07-03",
      "2025-07-21"
    ],
    "dates_per_cell_min": 19,
    "dates_per_cell_max": 21,
    "years": [
      2021,
      2022,
      2023,
      2024,
      2025
    ],
    "scenario_count_per_index": 24
  },
  "primary": {
    "ndre": {
      "index": "ndre",
      "scenario_id": "tau30_range150_common0p25",
      "tau_days": 30,
      "range_m": 150,
      "common_fraction": 0.25,
      "arithmetic_mean_slope": 0.0029431706189762545,
      "arithmetic_mean_se": 0.026760124383156208,
      "arithmetic_ci_lower": -0.049505709393822046,
      "arithmetic_ci_upper": 0.055392050631774555,
      "full_gls_slope": 0.010822795955357444,
      "full_gls_se": 0.020127269042595496,
      "independence_equivalent_cell_count": 3.3553844154233268,
      "covariance_condition": 314.4623576176432,
      "full_gls_negative_weight_mass": 1.2328706982134479,
      "full_gls_max_absolute_weight": 0.2169053312853421
    },
    "ndvi": {
      "index": "ndvi",
      "scenario_id": "tau30_range150_common0p25",
      "tau_days": 30,
      "range_m": 150,
      "common_fraction": 0.25,
      "arithmetic_mean_slope": 0.0049557120577748475,
      "arithmetic_mean_se": 0.04629738954022319,
      "arithmetic_ci_lower": -0.08578550401928402,
      "arithmetic_ci_upper": 0.0956969281348337,
      "full_gls_slope": 0.011452290639589082,
      "full_gls_se": 0.0408422515259441,
      "independence_equivalent_cell_count": 3.3479082888466127,
      "covariance_condition": 282.97694594694326,
      "full_gls_negative_weight_mass": 0.8794970293868171,
      "full_gls_max_absolute_weight": 0.12424320798970157
    }
  },
  "sensitivity": {
    "ndre": {
      "slope_min": 0.0014973609055566855,
      "slope_max": 0.006748624456177888,
      "se_min": 0.0005793437180893146,
      "se_max": 0.14772082057003041,
      "scenario_interval_envelope_lower": -0.2880301271784065,
      "scenario_interval_envelope_upper": 0.29102484898951986,
      "envelope_is_confidence_interval": false
    },
    "ndvi": {
      "slope_min": 0.003487943200222513,
      "slope_max": 0.008041148692703887,
      "se_min": 0.0008924330018772875,
      "se_max": 0.2561197365349216,
      "scenario_interval_envelope_lower": -0.49849751613811133,
      "scenario_interval_envelope_upper": 0.5054734025385563,
      "envelope_is_confidence_interval": false
    }
  },
  "simulations": [
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      "tau_days": 0,
      "range_m": 0,
      "common_fraction": 0,
      "method": "arithmetic_oracle",
      "replicates": 500,
      "empirical_coverage": 0.94,
      "coverage_monte_carlo_se": 0.010620734437881408,
      "coverage_mc_wilson_lower": 0.9156389368747802,
      "coverage_mc_wilson_upper": 0.9576516435190539,
      "mean_standard_error": 0.0005793437180893147,
      "empirical_standard_deviation": 0.0006098854747386001,
      "mean_estimate": -1.7943719685091643e-05,
      "known_null_slope": 0.0
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      "coverage_monte_carlo_se": 0.008763560920082661,
      "coverage_mc_wilson_lower": 0.9390264041651492,
      "coverage_mc_wilson_upper": 0.9739592026102227,
      "mean_standard_error": 0.25611973653492154,
      "empirical_standard_deviation": 0.2560031350475394,
      "mean_estimate": -0.011489673024076883,
      "known_null_slope": 0.0
    },
    {
      "index": "ndvi",
      "scenario_id": "tau365_range600_common0p25",
      "tau_days": 365,
      "range_m": 600,
      "common_fraction": 0.25,
      "method": "arithmetic_refit",
      "replicates": 500,
      "empirical_coverage": 0.954,
      "coverage_monte_carlo_se": 0.009368457717255283,
      "coverage_mc_wilson_lower": 0.9319222072317267,
      "coverage_mc_wilson_upper": 0.9691548916291838,
      "mean_standard_error": 0.25350968696080245,
      "empirical_standard_deviation": 0.2560031350475394,
      "mean_estimate": -0.011489673024076883,
      "known_null_slope": 0.0
    },
    {
      "index": "ndvi",
      "scenario_id": "tau365_range600_common0p25",
      "tau_days": 365,
      "range_m": 600,
      "common_fraction": 0.25,
      "method": "partition_oracle",
      "replicates": 500,
      "empirical_coverage": 0.954,
      "coverage_monte_carlo_se": 0.009368457717255283,
      "coverage_mc_wilson_lower": 0.9319222072317267,
      "coverage_mc_wilson_upper": 0.9691548916291838,
      "mean_standard_error": 0.2144855719227666,
      "empirical_standard_deviation": 0.21664984942137747,
      "mean_estimate": -0.007273269545516008,
      "known_null_slope": 0.0
    },
    {
      "index": "ndvi",
      "scenario_id": "tau365_range600_common0p25",
      "tau_days": 365,
      "range_m": 600,
      "common_fraction": 0.25,
      "method": "partition_refit",
      "replicates": 500,
      "empirical_coverage": 0.686,
      "coverage_monte_carlo_se": 0.020755914819636352,
      "coverage_mc_wilson_lower": 0.6440316011844003,
      "coverage_mc_wilson_upper": 0.7251321441639025,
      "mean_standard_error": 0.12184720515940317,
      "empirical_standard_deviation": 0.2430336050227577,
      "mean_estimate": -0.014642798686174128,
      "known_null_slope": 0.0
    }
  ],
  "inference_release": {
    "production_detector_changed": false,
    "confidence_method_validated": false,
    "partition_refit_minimum_simulated_coverage": 0.686,
    "interpretation": "Estimated-scale partition intervals show material undercoverage in these conditional null simulations. Neither partition nor full-GLS intervals are promoted as calibrated confidence. Arithmetic-mean intervals retain fixed weights but still rely on unvalidated covariance and mean-model assumptions.",
    "temporal_kernel_limit": "The temporal CAR(1) kernel has no measurement nugget. Strong correlation across close dates can inflate the fitted residual scale when observed day-to-day differences are large. The broad sensitivity intervals reflect this working-model assumption, not isolated spatial correction."
  },
  "output_sha256": {
    "scenarios.csv": "fa28e33b0a6fc95464886e56239c6075ec11d690b2be342ce06913187e215d13",
    "partitions.csv": "51a5e2b9e6bf9f574950e0bd59c926e966c96753f2dfe389bc7bd70cd90d0192",
    "cells.csv": "034f2c9d7e6035fa6f38d8d1389e446fcb2d6cc30ea19194cdd465041ea6bdd0",
    "simulations.csv": "843af16844439f0644c3826f4cbae1f1991d4a0f9ab6fa0ea4d8c4fb5f87e4b4"
  },
  "status": "research_only_conditional_working_model_not_detector_confidence"
}