{
  "title": "Cabinet door and drawer reveal data for AI inspection",
  "source_page": "https://fiund.com/industries/cabinet-reveal-inspection-data",
  "authorship": "Original fictional Fiund editorial exercise. No actual operating records or third-party source assets are included.",
  "example": {
    "title": "A dark edge is not necessarily a wider reveal",
    "context": "An original paper cabinet mockup is captured with front light and side light. A drawn scale supports a toy comparison.",
    "records": [
      [
        "1",
        "Reference view",
        "Left and right reveal targets 3 mm; no acceptance tolerance given."
      ],
      [
        "2",
        "Side-lit image",
        "Right edge appears dark and broad, but ruler shows 3 mm on both sides."
      ],
      [
        "3",
        "Front-lit image",
        "Dark region disappears; scaled gaps remain 3 mm on both sides."
      ]
    ],
    "question": "What should the model label about the right reveal?",
    "answer": "Both measured reveals are 3 mm in records 2 and 3, matching the reference value. The extra dark width changes with lighting and should be labeled as a visual artifact rather than measured gap asymmetry. The example does not specify an acceptance tolerance.",
    "failureModes": [
      "Using shadow width as a physical dimension",
      "Treating one image artifact as proof of misalignment"
    ],
    "fields": [
      [
        "lighting_view",
        "Documents capture conditions"
      ],
      [
        "calibrated_gap",
        "Separates physical measurement from appearance"
      ],
      [
        "side_id",
        "Aligns left and right observations"
      ]
    ],
    "holdout": "Give unequal scaled gaps under both lighting setups; score real asymmetry separately from shadow.",
    "boundary": "Use owned mockups; no actual cabinet drawings, customer homes, installation or quality grading."
  }
}