{
 "claim": "Four open reproductions of the Jev shape, four structurally different checkpoints. Every parameter count and tensor shape below is read from the actual safetensors header over an HTTP range request, not from the model card. The last row is Jev itself, for which the corresponding cells are the finding.",
 "method": "For each repo: GET bytes 0-7 of the safetensors file, read the 8-byte little-endian header length, GET the header JSON, sum the products of every tensor's shape. No weights were downloaded. Training and metric cells are quoted from each repo's committed README / open_jev_config.json / train_manifest.json / adapter_config.json, with its own stated n where it states one.",
 "source": "https://huggingface.co/com-kotobalabs/open-jev-deberta-v3-large",
 "captured": "2026-09-18",
 "note": "The parameter sums cross-check two of the cards: heman10x's train_manifest.json claims 151,378,177 parameters and the header sums to exactly that; DavidHatley's card says 69.3M and the header sums to 69,328,144. Nobody publishes a calibration number for Jev, so its ECE cell is empty in the sense of not existing, not in the sense of my not having found it \u2014 see the article for the search. Each row's backbone and training run stay in the committed JSON and in the prose; the table carries the six columns the argument turns on.",
 "columns": [
  {
   "key": "repo",
   "label": "checkpoint"
  },
  {
   "key": "adds",
   "label": "what the checkpoint adds"
  },
  {
   "key": "newparams",
   "label": "params",
   "align": "right"
  },
  {
   "key": "outspace",
   "label": "output space"
  },
  {
   "key": "loss",
   "label": "loss"
  },
  {
   "key": "ece",
   "label": "published ECE"
  }
 ],
 "rows": [
  {
   "repo": "com-kotobalabs/open-jev-deberta-v3-large",
   "base": "microsoft/deberta-v3-large",
   "adds": "head.safetensors: Linear[1024,3072] + Linear[1,1024]",
   "newparams": "3,147,777",
   "outspace": "1 scalar per option, softmax within the question",
   "trained": "18k states / 42k questions, 1 epoch, 1\u00d7H100, 229 s",
   "loss": "CE + Brier",
   "ece": "0.022 in-domain (n=3,508) / 0.035 OOD (n=4,012)"
  },
  {
   "repo": "heman10x/rlcd-modernbert-151m",
   "base": "knowledgator/gliclass-modern-base-v2.0 (ModernBERT)",
   "adds": "text + class projectors (2\u00d7768\u2192768\u2192768) + logit_scale",
   "newparams": "151,378,177 total, no option-shaped tensor",
   "outspace": "dot product per option description; nothing option-shaped",
   "trained": "3 epochs, batch 8 (eff. 32), MPS, 645.6 s",
   "loss": "CE + 1.0 \u00d7 Brier",
   "ece": "0.0352 \u2192 0.0335 after temperature (n=1,000)"
  },
  {
   "repo": "DavidHatley/system-one-mini",
   "base": "distilbert/distilbert-base-uncased",
   "adds": "5 heads: Linear[768,768] \u2192 Linear[k,768], k = 2,2,5,5,2",
   "newparams": "69,328,144 total",
   "outspace": "five fixed questions, fixed option sets",
   "trained": "20,000 synthetic states, 3 epochs, 1\u00d7RTX 3060, seed 1729",
   "loss": "not stated; checkpoint selected by validation mean NLL",
   "ece": "0.1273 raw \u2192 0.2673 after per-head temperature (final renderer)"
  },
  {
   "repo": "Foodoo1/Qwen3-14B-RLCD-Decision-LoRA",
   "base": "Qwen/Qwen3-14B (4-bit NF4)",
   "adds": "LoRA r=16 \u03b1=32 on 7 modules; modules_to_save: null",
   "newparams": "0 new output params",
   "outspace": "one vocabulary row, sliced to candidate tokens",
   "trained": "16,608 single-token examples, 1 epoch, 1\u00d7RTX 3090, ~97 min",
   "loss": "CE on the single decision token only",
   "ece": "none reported"
  },
  {
   "repo": "TypeSafe Jev 1.13",
   "base": "not disclosed \u2014 \"neither small nor an LLM\"",
   "adds": "not disclosed",
   "newparams": "not disclosed",
   "outspace": "\u2264255 Choice options / 2\u201310 Score levels / 1 Noul, per request",
   "trained": "not disclosed",
   "loss": "not disclosed",
   "ece": "none published anywhere"
  }
 ]
}