{
  "budget": {
    "fully_matched": true,
    "parameter_gap_ratio": 0.004394175008233867,
    "parameters_matched": true,
    "training_flops_gap_ratio": 0.0045303271861090065,
    "training_flops_matched": true
  },
  "evaluation": {
    "eval_transforms": [
      "normalize"
    ],
    "fingerprint": "db6ebcb5d1af59e6",
    "seed": 4771,
    "split_id": "cifar100:v1:stratified:seed4771:fraction1:53ff0b1659b048c7"
  },
  "excluded": [],
  "notice": "These verified repeated-run measurements apply only to the declared dataset, preprocessing, seeds, hardware, constraints, and priorities; they are not universal architecture benchmarks.",
  "project": "cifar100-random-init-controlled-comparison",
  "reasons": [
    "scratch-residual-cnn has the highest declared-priority score (0.6136).",
    "Its measured accuracy is 60.957%, corruption accuracy is 5.030%, and ECE is 0.135.",
    "The score margin over scratch-compact-vit is 0.0753."
  ],
  "scores": [
    {
      "architecture": "cnn",
      "architecture_prior": 0.0,
      "calibration": 0.864641025662,
      "latency": 1.0,
      "model_size": 0.9956058249917661,
      "name": "scratch-residual-cnn",
      "quality": 0.609566668669,
      "robustness": 0.0503000008563,
      "total": 0.6136217374207867,
      "weighted_evidence": 0.6136217374207867
    },
    {
      "architecture": "vit",
      "architecture_prior": 0.0,
      "calibration": 0.882109808425,
      "latency": 0.2993855944400667,
      "model_size": 1.0,
      "name": "scratch-compact-vit",
      "quality": 0.556166688601,
      "robustness": 0.129033329586,
      "total": 0.5383367064954067,
      "weighted_evidence": 0.5383367064954067
    }
  ],
  "selected": {
    "architecture": "cnn",
    "name": "scratch-residual-cnn"
  },
  "warnings": []
}