{
  "schema_version": "1.0",
  "study_id": "S13",
  "run_id": "S13-608719b7-eea568ba",
  "code_version": "608719b77b23e68f298f456d69b725aba42a8d16",
  "source_sha256": {
    "research_program/__init__.py": "5abb4829b477fd3068ee4c11dad2b59cd33e90aad06a17a5e9fb2f840a75abf1",
    "research_program/evaluation.py": "09a5d060c34b8cd1e5a3330d90f5ff3e12adbca8cfe1ebe3297da2b91d227e45",
    "research_program/io.py": "16de123c16d80bce5eb8d6766224dec1729d7c00449c2528f2348d86ad68d35f",
    "research_program/validate.py": "3e6a591e2b77799bdf73683db438b2998e2e457340626c98b7ceed510ab3329e",
    "studies/S13/config.json": "ca811af6bb5b244d18a7b45585b7fb630599717ef85a3b54c69a32cfad3bd4a1",
    "studies/S13/ingest.py": "200f4e362ca3ae641f61dcd0972cd5f83ff6a11af8bcccd8de1428dd8492bf1e",
    "studies/S13/models.py": "1078308baa4af3983738cae374ed7beafc21d7eb4a925b34754609a8ed22a778",
    "studies/S13/study.py": "8928b2ce70803371898fbccd411e7ee785ff549a932e17f597f92c02d618b4c8",
    "uv.lock": "15f381c7c88897e1df049af6297905625b6869a6c9ff110ab1647ec2d0f305f4"
  },
  "evaluated_on": "2026-09-29",
  "data": {
    "source_url": "https://archive.ics.uci.edu/dataset/179/secom",
    "release": "SECOM original files; retrieved September 29, 2026",
    "sha256": "eea568baf3c2229096d7d294cf0b096b5502bd96d92c0b80a65b84714059be8e",
    "license": "CC BY 4.0",
    "citation": "McCann, M. & Johnston, A. (2008). SECOM [Dataset]. UCI Machine Learning Repository. doi:10.24432/C54305.",
    "file_sha256": {
      "secom.data": "20f0e7ee434f7dcbae0eea9ffff009a2b57f42d6b0dc9a5bd4f00782c0a3374c",
      "secom_labels.data": "126884cf453705c9e61a903fe906f0665a3b45ce3639e621edc5c93c89627e03"
    }
  },
  "target": {
    "label": "Publisher +1 failure versus −1 pass",
    "estimand": "Retrospective failure screening, conditional on availability of recorded sensor measurements",
    "prediction_cutoff": "At an assumed screening point with recorded sensor measurements; pre-test acquisition times are not independently documented",
    "horizon": "Associated in-house quality test; no verified advance-warning horizon",
    "unit": "One production entity/source row"
  },
  "cohort": "October 1–17, 2008 final entities, following July–August development and September validation; original calendar retained",
  "samples": {
    "total": 1567,
    "total_failures": 104,
    "development": 618,
    "development_failures": 65,
    "validation": 590,
    "validation_failures": 17,
    "final_fit": 1208,
    "final_fit_failures": 82,
    "test": 359,
    "test_failures": 22,
    "test_days": 17
  },
  "models": [
    {
      "id": "elastic_net",
      "label": "Sparse elastic-net logistic",
      "specification": "Frozen elastic_net; selected setting 0.1; training-only preprocessing. See protocol."
    },
    {
      "id": "pca_monitor",
      "label": "PCA process monitor",
      "specification": "Frozen pca_monitor; selected setting 5; training-only preprocessing. See protocol."
    },
    {
      "id": "boosting",
      "label": "Gradient boosting",
      "specification": "Frozen boosting; selected setting 7; training-only preprocessing. See protocol."
    },
    {
      "id": "boosting_no_missing",
      "label": "Boosting without missingness indicators",
      "specification": "Frozen boosting_no_missing; selected setting 7; training-only preprocessing. See protocol."
    },
    {
      "id": "prevalence",
      "label": "Training failure prevalence",
      "specification": "Constant final-training prevalence"
    }
  ],
  "metrics": [
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      "model": "elastic_net",
      "name": "average_precision",
      "estimate": 0.076527,
      "unit": "proportion",
      "split": "final",
      "lower": 0.03447771,
      "upper": 0.17479158,
      "confidence": 0.95
    },
    {
      "model": "elastic_net",
      "name": "roc_auc",
      "estimate": 0.53560831,
      "unit": "proportion",
      "split": "final",
      "lower": 0.37720188,
      "upper": 0.68057086,
      "confidence": 0.95
    },
    {
      "model": "elastic_net",
      "name": "log_loss",
      "estimate": 0.25504353,
      "unit": "natural-log units",
      "split": "final",
      "lower": 0.12529742,
      "upper": 0.39097509,
      "confidence": 0.95
    },
    {
      "model": "elastic_net",
      "name": "brier",
      "estimate": 0.06015363,
      "unit": "proportion",
      "split": "final",
      "lower": 0.02737666,
      "upper": 0.09407077,
      "confidence": 0.95
    },
    {
      "model": "pca_monitor",
      "name": "average_precision",
      "estimate": 0.06600792,
      "unit": "proportion",
      "split": "final",
      "lower": 0.03179912,
      "upper": 0.13351209,
      "confidence": 0.95
    },
    {
      "model": "pca_monitor",
      "name": "roc_auc",
      "estimate": 0.47167521,
      "unit": "proportion",
      "split": "final",
      "lower": 0.3581266,
      "upper": 0.53646576,
      "confidence": 0.95
    },
    {
      "model": "pca_monitor",
      "name": "log_loss",
      "estimate": 0.23582129,
      "unit": "natural-log units",
      "split": "final",
      "lower": 0.14475165,
      "upper": 0.32845556,
      "confidence": 0.95
    },
    {
      "model": "pca_monitor",
      "name": "brier",
      "estimate": 0.05846131,
      "unit": "proportion",
      "split": "final",
      "lower": 0.02802751,
      "upper": 0.0895663,
      "confidence": 0.95
    },
    {
      "model": "boosting",
      "name": "average_precision",
      "estimate": 0.0625722,
      "unit": "proportion",
      "split": "final",
      "lower": 0.03172822,
      "upper": 0.09925331,
      "confidence": 0.95
    },
    {
      "model": "boosting",
      "name": "roc_auc",
      "estimate": 0.48637712,
      "unit": "proportion",
      "split": "final",
      "lower": 0.38551462,
      "upper": 0.59411109,
      "confidence": 0.95
    },
    {
      "model": "boosting",
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      "split": "final",
      "lower": 0.12252014,
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      "confidence": 0.95
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    {
      "model": "boosting",
      "name": "brier",
      "estimate": 0.0594379,
      "unit": "proportion",
      "split": "final",
      "lower": 0.02512919,
      "upper": 0.09432986,
      "confidence": 0.95
    },
    {
      "model": "boosting_no_missing",
      "name": "average_precision",
      "estimate": 0.06319915,
      "unit": "proportion",
      "split": "final",
      "lower": 0.03123412,
      "upper": 0.10198454,
      "confidence": 0.95
    },
    {
      "model": "boosting_no_missing",
      "name": "roc_auc",
      "estimate": 0.48219585,
      "unit": "proportion",
      "split": "final",
      "lower": 0.36737722,
      "upper": 0.59340734,
      "confidence": 0.95
    },
    {
      "model": "boosting_no_missing",
      "name": "log_loss",
      "estimate": 0.25863677,
      "unit": "natural-log units",
      "split": "final",
      "lower": 0.12407434,
      "upper": 0.39705881,
      "confidence": 0.95
    },
    {
      "model": "boosting_no_missing",
      "name": "brier",
      "estimate": 0.05936449,
      "unit": "proportion",
      "split": "final",
      "lower": 0.02511887,
      "upper": 0.09407868,
      "confidence": 0.95
    },
    {
      "model": "prevalence",
      "name": "average_precision",
      "estimate": 0.06128134,
      "unit": "proportion",
      "split": "final",
      "lower": 0.02517341,
      "upper": 0.0977096,
      "confidence": 0.95
    },
    {
      "model": "prevalence",
      "name": "roc_auc",
      "estimate": 0.5,
      "unit": "proportion",
      "split": "final",
      "lower": 0.5,
      "upper": 0.5,
      "confidence": 0.95
    },
    {
      "model": "prevalence",
      "name": "log_loss",
      "estimate": 0.23083375,
      "unit": "natural-log units",
      "split": "final",
      "lower": 0.13624155,
      "upper": 0.32626515,
      "confidence": 0.95
    },
    {
      "model": "prevalence",
      "name": "brier",
      "estimate": 0.05756949,
      "unit": "proportion",
      "split": "final",
      "lower": 0.02636363,
      "upper": 0.08905219,
      "confidence": 0.95
    }
  ],
  "uncertainty": "1,000 paired calendar-day cluster bootstrap draws; 95% percentile intervals condition on the fitted models and 17 observed test days. Two-day circular-block sensitivity is supplementary. Sensor stability uses 30 training-day bootstrap refits.",
  "assumptions": [
    "Recorded sensors are hypothetically available at screening; the source cannot verify their exact pre-test acquisition timing.",
    "Capacity selects the top-scored entities across the complete final batch, not a daily production schedule.",
    "Calendar days proxy dependence; unobserved wafer/lot groups cannot be reconstructed."
  ],
  "limitations": [
    "Only 22 final failures and 17 final days support limited precision.",
    "Anonymous sensors and missing acquisition times prevent root-cause and validated early-warning claims.",
    "A short historical sample with changing prevalence does not validate a modern production line.",
    "Intervals condition on fitted models and cannot account for unrecorded batch dependence.",
    "Selection stability is descriptive and does not establish causal or physical sensor importance.",
    "Inspection counts are historical ranking outcomes, not measured savings or avoided failures.",
    "Independent technical review is pending."
  ],
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      "actual_sensor_columns": 590,
      "documented_sensor_columns": 591,
      "missing_cells": 41951,
      "constant_columns": 116,
      "duplicate_columns": 104,
      "duplicate_sensor_rows": 0,
      "duplicate_timestamps": 33,
      "columns_over_half_missing": 28,
      "months": [
        {
          "month": "2008-07",
          "entities": 63,
          "failures": 14
        },
        {
          "month": "2008-08",
          "entities": 555,
          "failures": 51
        },
        {
          "month": "2008-09",
          "entities": 590,
          "failures": 17
        },
        {
          "month": "2008-10",
          "entities": 359,
          "failures": 22
        }
      ],
      "source_timestamp_min": "2008-07-19 11:55:00",
      "source_timestamp_max": "2008-10-17 06:07:00"
    },
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