Pith Number
pith:ZSLRFHIP
pith:2024:ZSLRFHIPCC52SPW4QZN26G3H6R
not attested
not anchored
not stored
refs pending
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
arxiv:2408.11854 v2 · 2024-08-15 · cs.CL · cs.AI · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{ZSLRFHIPCC52SPW4QZN26G3H6R}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
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Bitcoin timestamp
2
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3
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4
Citations
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Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
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Receipt and verification
| First computed | 2026-07-05T09:09:25.195384Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
cc97129d0f10bba93edc865baf1b67f45bebd11afd7d16baf284fb1d315b2d3a
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZSLRFHIPCC52SPW4QZN26G3H6R \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: cc97129d0f10bba93edc865baf1b67f45bebd11afd7d16baf284fb1d315b2d3a
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "1e6d4f2f85e09d27731aded88bb3cd9217397134bd2c1433a34fd85bead66006",
"cross_cats_sorted": [
"cs.AI",
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
"primary_cat": "cs.CL",
"submitted_at": "2024-08-15T03:56:40Z",
"title_canon_sha256": "43c30c78960cf52d26145dc95fae3be57692e1c050756c4c1d031e863dcd1586"
},
"schema_version": "1.0",
"source": {
"id": "2408.11854",
"kind": "arxiv",
"version": 2
}
}