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          "precision": 8.755813953488367,
          "recall": -43.95323346729997,
          "f1": -20.197044334975367,
          "accuracy": -7.122173634130813
        }
      }
    },
    "range_region": {
      "simple": {
        "original_pilot_test": {
          "n": 522,
          "positive": 361,
          "tp": 335,
          "fp": 118,
          "fn": 26,
          "tn": 43,
          "precision": 0.739514348785872,
          "recall": 0.9279778393351801,
          "f1": 0.8230958230958231,
          "accuracy": 0.7241379310344828
        },
        "expansion_minus_original_percentage_points": {
          "precision": -8.633515803442693,
          "recall": -35.29142261036279,
          "f1": -21.145847532856997,
          "accuracy": -4.056571493745964
        }
      },
      "geometry": {
        "original_pilot_test": {
          "n": 522,
          "positive": 361,
          "tp": 266,
          "fp": 42,
          "fn": 95,
          "tn": 119,
          "precision": 0.8636363636363636,
          "recall": 0.7368421052631579,
          "f1": 0.7952167414050823,
          "accuracy": 0.7375478927203065
        },
        "expansion_minus_original_percentage_points": {
          "precision": -18.7960687960688,
          "recall": -41.87759475023436,
          "f1": -36.26907898479888,
          "accuracy": -9.91796457522801
        }
      },
      "learned": {
        "original_pilot_test": {
          "n": 522,
          "positive": 361,
          "tp": 361,
          "fp": 161,
          "fn": 0,
          "tn": 0,
          "precision": 0.6915708812260536,
          "recall": 1.0,
          "f1": 0.8176670441676104,
          "accuracy": 0.6915708812260536
        },
        "expansion_minus_original_percentage_points": {
          "precision": -22.139057609429212,
          "recall": -13.740458015267176,
          "f1": -20.904944991267328,
          "accuracy": -17.227650415879893
        }
      }
    }
  },
  "baselines": {
    "obstacle": {
      "all_positive": {
        "n": 1563,
        "positive": 322,
        "negative": 1241,
        "tp": 322,
        "fp": 1241,
        "fn": 0,
        "tn": 0,
        "precision": 0.20601407549584133,
        "recall": 1.0,
        "f1": 0.3416445623342175,
        "accuracy": 0.20601407549584133
      },
      "all_negative": {
        "n": 1563,
        "positive": 322,
        "negative": 1241,
        "tp": 0,
        "fp": 0,
        "fn": 322,
        "tn": 1241,
        "precision": null,
        "recall": 0.0,
        "f1": 0.0,
        "accuracy": 0.7939859245041587
      }
    },
    "range_region": {
      "all_positive": {
        "n": 907,
        "positive": 393,
        "negative": 514,
        "tp": 393,
        "fp": 514,
        "fn": 0,
        "tn": 0,
        "precision": 0.43329658213891953,
        "recall": 1.0,
        "f1": 0.6046153846153847,
        "accuracy": 0.43329658213891953
      },
      "all_negative": {
        "n": 907,
        "positive": 393,
        "negative": 514,
        "tp": 0,
        "fp": 0,
        "fn": 393,
        "tn": 514,
        "precision": null,
        "recall": 0.0,
        "f1": 0.0,
        "accuracy": 0.5667034178610805
      }
    }
  },
  "paths": {
    "evaluated": false,
    "reason": "Expansion annotations have no path centerlines; do not reuse pilot path metrics as new-data evidence"
  },
  "caveats": [
    "Assistant source-image labels await owner review; these are not human-confirmed ground truth.",
    "Only clear supplied cell labels are scored. Unknown cells and all selection-only frames are excluded.",
    "Twenty labelled frames come from only three recordings, with repeated matches and obstacle layouts; cells and frames are correlated.",
    "All expansion recordings are absent from the original pilot. The preserved future split tags did not control this frozen-only evaluation.",
    "Comparison with the historical test describes a different sampled scene mix and label prevalence, not a paired improvement or significance test.",
    "No model, threshold or label was changed based on these predictions. After examining the results, this collection is development evidence for any subsequent algorithm changes.",
    "Range labels describe the union of visible yellow/green regions, not whose range it is or movement legality; obstacle labels describe visible environmental footprints, not verified collision state."
  ]
}