{
  "temperature": 1.0,
  "fitted_temperature_folded": 1.0403540135054734,
  "temperature_folded": true,
  "folded_tensor": "output_norm.weight",
  "source_calibration": {
    "temperature": 1.0403540135054734,
    "calibration_n": 3100,
    "objective": "sample_mean_soft_cross_entropy",
    "bounds": [
      0.05,
      20
    ],
    "nll_before": 0.5142612871187658,
    "nll_after": 0.5139652439657095,
    "backend": "gguf",
    "model": "results/Jev-Style-v2-Q8_0.gguf",
    "source_sha256": "2fde7f45dce3440abfde145bb30ad61e2643b1f853866b5760b235685328dc1c"
  },
  "input_sha256": "f659d164d0fed4e645f711cbf56c177ee63c1856e6875f48a27ebac2cfb11175",
  "output_sha256": "5c2aa0d35b24a27f03228b2c62ebaaebd9b5b785844634d4217278d822751494",
  "validation_required": false,
  "validation": {
    "n": 500,
    "argmax_agreement": 0.992,
    "cuda_same_subset": {
      "accuracy": 0.7910177949703642,
      "macro_f1": 0.7760857891780776,
      "nll": 0.6000106706289864,
      "brier": 0.317876961372947,
      "ece": 0.16696329399611096
    },
    "q8_same_subset": {
      "accuracy": 0.7868855635654054,
      "macro_f1": 0.773615445189482,
      "nll": 0.6015417570028033,
      "brier": 0.318930434743987,
      "ece": 0.16630794459650552
    },
    "accuracy_difference": -0.004132231404958775,
    "nll_difference": 0.0015310863738169367,
    "temperature_folded": true,
    "passed": true,
    "practical_deployment_gate_passed": true,
    "initial_strict_accuracy_target_met_on_subset": false,
    "initial_accuracy_loss_target": 0.003,
    "note": "500-example subset check: 99.2% agreement. Observed task-macro loss is 0.413 pp, so the initial 0.3 pp accuracy goal is not met on this subset. Prefer BF16/MLX when accuracy is the priority; this is not a bound on population degradation."
  }
}