{
  "project": "Fouling Forecast",
  "data": "Entirely synthetic; no refinery measurements",
  "data_seed": 20261105,
  "seeds": 5,
  "origin_day": 20,
  "end_day": 110,
  "horizon_days": 90,
  "threshold": 0.8,
  "split_counts": {
    "calibration": 20,
    "nominal": 20,
    "shift": 24,
    "train": 24
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  "learning_sizes": [
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    4,
    8,
    16
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  "interval_method": "8 campaign bootstrap grey fits, calibrated by maximum standardized temperature error across each calibration campaign; all other models use unscaled campaign maxima. 20 calibration campaigns, order statistic 20 of 20 for nominal 95% simultaneous trajectory coverage. Guarantees assume exchangeability and do not extend to shifted campaigns.",
  "timing_metric": "MAE only among eligible campaigns where both true and forecast crossing occur; always read alongside crossing recall/precision. Right-censored events remain NaN. Campaigns already below threshold at issue day excluded from event metrics.",
  "future_conditions": "All predictions hold last-five-observation median operating conditions fixed; future measured controls never provided.",
  "training_label": "Fouling rate estimated by inversion and smoothing of training sensors; no generator rate or true resistance enters learner. Calibration uses noise-free simulator temperature as evaluation target.",
  "version": {
    "numpy": "2.5.4",
    "scipy": "1.18.1",
    "pandas": "3.1.1",
    "scikit-learn": "1.9.1"
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  "python": "3.12.11",
  "runtime_seconds": 56.74,
  "source_sha256": "db8482868a5707c29f03f46626cf15bbc60c24b743eb25afea6a2ed54c8246c3",
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