{
  "analysis": "Scripps current-vintage sensitivity, not original author code replay",
  "timestamp_utc": "2026-11-05T10:58:13.156602+00:00",
  "environment": {
    "python": "3.12.14 (main, Aug 25 2026, 14:01:42) [MSC v.1944 64 bit (AMD64)]",
    "numpy": "2.3.5",
    "platform": "Windows-11-10.0.26300-SP0"
  },
  "script_sha256": "f4e397463ccd84fe665e574194a40394ae2dc7d6c3b77967fca433f3c116b8ff",
  "data_hashes": {
    "monthly_flask_c13_ljo.csv": "6aa6b39017b3016ca53f13b01b843c8ba4081b327a513d6fe5044bb04fde2f67",
    "monthly_flask_c13_mlo.csv": "de34d5fc0ba34923ba7c2f0a765d401f01c888cd54e3c4122d4ed3677d628368",
    "monthly_flask_c13_ptb.csv": "debcec028ac19b959184cc43830ea8699378e4836a0eb98179f45a7266de7723",
    "monthly_flask_c13_spo.csv": "9b739e9e740e2c520300ca807d9e324f8cf3ed9541fa60bbae2eb7a6aad0de1b",
    "monthly_flask_co2_mlo.csv": "e1efa8d790588949a00fb966e62252744d613d5a897645ff695019d7fee1eadc",
    "monthly_merge_co2_ljo.csv": "5dae17141422c4fd905dd210364ba5d7cab16855ab4937d738e8fb77a0038342",
    "monthly_merge_co2_ptb.csv": "42b8f27d76fa1f482db1a96d4b45f97a97f18d8fda18b32d5ec42f61ec76f342",
    "monthly_merge_co2_spo.csv": "fcc8d2f1a3891636971548d0df6a2ea9532213609870003556ca66ad1bbcb5ff"
  },
  "methods": {
    "missing": "Only finite co2>250 ppm and -20<delta<0; no imputation in raw/adjusted modes. Filled modes use publisher supplied cols9/10.",
    "annual": "Means over SAME paired months; minimum9/months peryear.",
    "regression": "OLS delta vs1000/CO2, centered x; intercept is x=0.",
    "uncertainty": "IID and Newey-West Bartlett lag12 monthly/lag3 annual; normal95 descriptive intervals. Circular paired 5-year block bootstrap2000replicates for baseline annual; seed20261105. These omit x-error/calibration, do not establish source-identification error, and are not a trend stationarity test.",
    "windows": "Fixed1978-2022 baseline, extended2023/2024, split1985-2000/2001-2022; use available paired observations only.",
    "abundance": "q=R/(1+R), R=.0111802*(1+delta/1000). Fit q vs1/C then transform fitted intercept back to delta. This checks small-ratio sensitivity, not physical identification."
  },
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      "iid_se": 0.04386086884243542,
      "hac_se": 0.11188849157314243,
      "hac_lag": 12,
      "hac_normal_95_interval": [
        -12.926008457671903,
        -12.487405570705183
      ],
      "residual_lag1_correlation": 0.6964825260590458,
      "station": "spo",
      "mode": "filled_adjusted",
      "aggregation": "monthly",
      "window": [
        2001,
        2022
      ],
      "first": [
        2001.0,
        1.0
      ],
      "last": [
        2021.0,
        12.0
      ]
    },
    {
      "n": 21,
      "intercept_permil": -12.704220201173078,
      "slope_permil_per_1000_over_ppm": 1.7265450298282519,
      "r2": 0.9841688994701355,
      "iid_se": 0.1292268256578573,
      "hac_se": 0.1515119499412514,
      "hac_lag": 3,
      "hac_normal_95_interval": [
        -13.00118362305793,
        -12.407256779288225
      ],
      "residual_lag1_correlation": 0.6542109244423489,
      "station": "spo",
      "mode": "filled_adjusted",
      "aggregation": "annual_at_least_9_paired_months",
      "window": [
        2001,
        2022
      ],
      "first_year": 2001.0,
      "last_year": 2021.0,
      "abundance_fit_intercept_permil": -12.704003316110324
    }
  ],
  "naive_pool": {
    "n": 160,
    "intercept_permil": -13.352507412743062,
    "slope_permil_per_1000_over_ppm": 1.9471694309855605,
    "r2": 0.9557257718430188,
    "iid_se": 0.0895303417486068,
    "hac_se": 0.0889361562386118,
    "hac_lag": 0,
    "hac_normal_95_interval": [
      -13.52682227897074,
      -13.178192546515383
    ],
    "residual_lag1_correlation": 0.9172622251879462,
    "warning": "Naive unequal-station pooled annual OLS; no independent-station inference or valid pooled HAC."
  }
}