Pith Number
pith:H3RQRDZL
pith:2024:H3RQRDZLUFK4RP232HJY5LFRWA
not attested
not anchored
not stored
refs pending
Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction
arxiv:2411.04055 v1 · 2024-11-06 · cs.LG · cs.CV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{H3RQRDZLUFK4RP232HJY5LFRWA}
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
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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.
Cited by
Receipt and verification
| First computed | 2026-07-05T09:32:00.160269Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
3ee3088f2ba155c8bf5bd1d38eacb1b02db845df2d045436c568e536626cbfe5
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/H3RQRDZLUFK4RP232HJY5LFRWA \
| 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: 3ee3088f2ba155c8bf5bd1d38eacb1b02db845df2d045436c568e536626cbfe5
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "2ce8f2313ec28838f998518b0b057f5ffe32d2ce47599062c7ed4a5d761f2338",
"cross_cats_sorted": [
"cs.CV"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2024-11-06T16:59:51Z",
"title_canon_sha256": "dd42cf9466e58017e268cc55f7f00db541da479b38652ef7398bb5c41efdcb23"
},
"schema_version": "1.0",
"source": {
"id": "2411.04055",
"kind": "arxiv",
"version": 1
}
}