{
  "title": "Building inspection moisture observations and image data for AI",
  "source_page": "https://fiund.com/industries/inspection-moisture-observation-data",
  "authorship": "Original fictional Fiund editorial exercise. No actual operating records or third-party source assets are included.",
  "example": {
    "title": "A discoloration photo and a measurement from another panel do not establish one cause",
    "context": "Two original practice wall panels use harmless marks and invented readings. The example makes no actual mold, environmental-health, property or occupant assessment.",
    "records": [
      [
        "BMV1",
        "Reference image",
        "Panel A has a visible brown practice mark at region R; A label readable."
      ],
      [
        "BMV2",
        "Mock measurement",
        "Panel B region S records value 6 arbitrary units under method M. No A measurement supplied."
      ],
      [
        "BMV3",
        "Draft report",
        "Combines A's image with B's value and calls the cause confirmed. No qualified cause finding or source link supports that sentence."
      ]
    ],
    "question": "Which panel supports each observation, and does the combined draft establish the cause of A's visible mark?",
    "answer": "BMV1 supports visible discoloration on A/R, while BMV2's value belongs to B/S under its stated method. BMV3 merges different panels and supplies no independent cause finding. It therefore cannot establish what caused A's mark or any health condition. Preserve the image and measurement identities separately, flag the unsupported join and refer the missing evidence for appropriate review.",
    "failureModes": [
      "Combining a photo and measurement from different locations into one diagnosis.",
      "Treating visible discoloration as proof of a specific hidden cause."
    ],
    "fields": [
      [
        "Panel and region identity",
        "Prevents cross-location joins."
      ],
      [
        "Measurement method and unit",
        "Retains the scope of the mock reading."
      ],
      [
        "Observation versus cause finding",
        "Separates appearance evidence from an unsupported interpretation."
      ]
    ],
    "holdout": "Provide A's own measurement but no cause review. Require the model to resolve source matching while still withholding the claimed cause.",
    "boundary": "Use original images and unoccupied props. Exclude addresses, occupants, real customer reports and actual environmental samples."
  }
}