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  ],
  "budget": {
    "cap_usd": 50.0,
    "reserved_upper_usd": 39.666098,
    "reservation_count": 60,
    "reservation_ceiling_usd": 42.0,
    "latest_metered_usage": {
      "metered_cost_usd": 27.80917224,
      "queried_unix": 1788985916.025095
    },
    "ledger_sha256": "4f9b1b7ae619c9eb8ed5718ed50434c2e485b76bd973d7f491009678f19bca52",
    "interpretation": "Reservations are conservative resource allocations, not invoices or measured spend. Metered usage is a timestamped potentially lagging snapshot, not a final invoice."
  },
  "limitations": [
    "Few final training seeds; intervals are fragile and unadjusted across primary/secondary comparisons.",
    "Learning-rate selection uncertainty and heldout dataset uncertainty are not included in seed intervals; tuning uses its recorded independent seed count.",
    "ConvNeXt and ViT same-seed initial weights can differ by CPU host. Separate post hoc audits accept only two measured, numerically close hashes per architecture/seed with matching post-init RNG; identical training trajectories are not established. GPT initialization remains bit-identically paired.",
    "Compute-then-mask executes dense branches; these runs do not establish FLOP, latency, memory, energy, or dollar savings.",
    "Peak reserved GPU memory can include allocator cache from previous calls or prevalidation; it is not the model's required VRAM. Peak allocated memory describes live tensors in this implementation, including resident data.",
    "Two-thirds retained residual blocks does not imply two-thirds FLOPs; ConvNeXt stage transitions always remain.",
    "Fixed image resizing adds no observed information; CIFAR spatial geometry differs from ImageNet.",
    "Small ConvNeXt LayerScale and undertraining can make deletion appear harmless; inspect full learning and recorded gamma magnitudes.",
    "Language uses its recorded fixed document-contained context windows, not canonical full-corpus perplexity.",
    "This is training from scratch under a short declared compute budget, not a reproduction of the target paper's LLM training scale."
  ]
}