{
  "disclosure": "SYNTHETIC DEMONSTRATION. Fictional operating history, material variation, measurements and changed condition; no employer/customer data.",
  "version": "ml-1.0",
  "seed": 581037,
  "factory": "SYN-OPS-01",
  "part": "SYN-VS25",
  "machine": "SYN-MC-01",
  "station": "SYN-ST-M01",
  "batchCount": 720,
  "piecesPerBatch": 24,
  "pieceCount": 17280,
  "target": "One or more of 24 final diameters strictly outside 24.90\u201325.10 mm",
  "cutoff": "Before batch machining begins",
  "features": [
    "toolCuts",
    "feedMmRev",
    "speedMMin",
    "coolantLMin",
    "coolantTempC",
    "setupOffsetUm",
    "hardnessHB"
  ],
  "excluded": [
    "final diameters",
    "defect count",
    "label",
    "downstream dispositions",
    "batch sequence",
    "split",
    "hidden condition"
  ],
  "splits": [
    {
      "split": "train",
      "start": 1,
      "end": 432,
      "batches": 432,
      "positives": 26,
      "missingInputs": 69
    },
    {
      "split": "validation",
      "start": 433,
      "end": 576,
      "batches": 144,
      "positives": 5,
      "missingInputs": 14
    },
    {
      "split": "holdout",
      "start": 577,
      "end": 720,
      "batches": 144,
      "positives": 23,
      "missingInputs": 18
    }
  ],
  "changedCondition": "Batch 625 onwards: fictional unobserved +18 \u00b5m thermal offset and coolant reading overstates actual flow by 1.2 L/min. Missingness also increases. Condition is evaluation metadata, never a model input.",
  "scope": "Separate long synthetic learning history; not an inventory ledger, weekly throughput claim, DOE reuse or confirmation evidence.",
  "versions": {
    "python": "3.12.14",
    "numpy": "2.3.5",
    "sklearn": "1.7.2"
  },
  "sha256": {
    "batch-history.csv": "3648630120decf6bedb5513d7680e55c1d71e98b93a0821cd2a781e599a61145",
    "piece-inspection.csv": "967946aa4c670bde0bf93411ef463b6eaab9b50d493dd245f5cc7d5c261b315d",
    "trained-models.json": "2c01487c09b8c09728cefc136476cb907950e179d022dcefc0e3fe1c6053cd38",
    "reference-predictions.json": "d6fe7c4d23bc321723f472dc8030718956647631af556086c02d381a24921c56",
    "evaluation.json": "3ba39859f4196d5fe34d17167ee309e34fd0a7f871e343d023b316013b28bde8"
  }
}