Pith Number
pith:SXJ2IZQZ
pith:2024:SXJ2IZQZPDL5F7K5TNFRVHGYUB
not attested
not anchored
not stored
refs pending
Improving Medical Multi-modal Contrastive Learning with Expert Annotations
arxiv:2403.10153 v3 · 2024-03-15 · cs.CV · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{SXJ2IZQZPDL5F7K5TNFRVHGYUB}
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.
Cited by
Receipt and verification
| First computed | 2026-07-05T08:43:53.958134Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
95d3a4661978d7d2fd5d9b4b1a9cd8a075446cc517c7f7bf7e468707815dbfa4
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/SXJ2IZQZPDL5F7K5TNFRVHGYUB \
| 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: 95d3a4661978d7d2fd5d9b4b1a9cd8a075446cc517c7f7bf7e468707815dbfa4
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "1d80345902fbf130d004439c27a03a8ae8cee52d7e5b602075c9cbb51f3ab08a",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2024-03-15T09:54:04Z",
"title_canon_sha256": "37dd20fde10854f4b3d6cf8f32ae9f160fa0009b55a1e8eb21b4b58c6bc14f04"
},
"schema_version": "1.0",
"source": {
"id": "2403.10153",
"kind": "arxiv",
"version": 3
}
}