Pith Number
pith:TOTF2AHV
pith:2016:TOTF2AHVYKV2GEJQRIFYWXEDQW
not attested
not anchored
not stored
refs pending
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
arxiv:1602.02685 v2 · 2016-02-08 · cs.LG · cs.AI · cs.NE
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{TOTF2AHVYKV2GEJQRIFYWXEDQW}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
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.
Receipt and verification
| First computed | 2026-05-18T00:58:04.266911Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
9ba65d00f5c2aba311308a0b8b5c8385b0fa796b53de10f1200f8a0191297281
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/TOTF2AHVYKV2GEJQRIFYWXEDQW \
| 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: 9ba65d00f5c2aba311308a0b8b5c8385b0fa796b53de10f1200f8a0191297281
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "be9d431f0ba7867a90ba73a6f31784b415adb8df1f8e8c33d80409c0ce294218",
"cross_cats_sorted": [
"cs.AI",
"cs.NE"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2016-02-08T18:30:58Z",
"title_canon_sha256": "9d8e9253549357d19574c003d67abd405f9e778f1deab5b188b101d9744b3d1b"
},
"schema_version": "1.0",
"source": {
"id": "1602.02685",
"kind": "arxiv",
"version": 2
}
}