{
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  "volumetric_evidence": {
    "classification": "historical_lab_systems_evidence",
    "model": "STU-Net-S",
    "cases": [
      64,
      96
    ],
    "training_seeds": 2,
    "completed_jobs": 4,
    "updates_per_job": 1536,
    "clinical_validation": false
  },
  "sources": [
    {
      "label": "summary.json",
      "sha256": "54e85277f4cef947cb44dbcc1c6384a3a162a8ccf929729646af24e318328043"
    },
    {
      "label": "events.jsonl",
      "sha256": "9005e017512090e4fb1cb411a2fcd23b9a786cbc95e6f42cbb209dd8a63e0d2f"
    },
    {
      "label": "environment.json",
      "sha256": "a740ec30f32690d54ae86c396e52bd4536f8cbbc6276d779e5b0c1bbe1f9efe0"
    },
    {
      "label": "plan.json",
      "sha256": "92d1e708fca5c9bed4be9ee1eea23e4bab22811edf6ee2d34401bce6ad34a96d"
    },
    {
      "label": "report-summary.json",
      "sha256": "665730bdce7047f5abd2cb8600db551f2a5522763b2660d2dc67c29daae1a0f6"
    },
    {
      "label": "STU-Net two-seed aggregate audit",
      "sha256": "74355d519fea3d3af4c55b486552d858d1578cf1ebc1c52fb90a10f15f9b4e1f"
    }
  ],
  "limits": [
    "The browser replays saved events; it does not execute GPU training.",
    "Tiny3D uses synthetic examples; its loss and memory do not describe STU-Net or clinical performance.",
    "The 4-GiB hardware capacity differs from PyTorch reserved memory.",
    "Research supervisors and the packaged runtime are distinct execution paths.",
    "General recovery/resource guarantees and long STU-Net prefetch equivalence remain unproven.",
    "No industrial pilot or commercial savings have been verified."
  ]
}