Pith Number
pith:K3DC5G3S
pith:2025:K3DC5G3SY57URD2GZKODGSD5LS
not attested
not anchored
not stored
refs pending
Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning
arxiv:2509.00745 v1 · 2025-08-31 · cs.CV · cs.AI · cs.CY · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{K3DC5G3SY57URD2GZKODGSD5LS}
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 | 2026-07-05T12:02:22.133692Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
56c62e9b72c77f488f46ca9c33487d5c99dd81a50f43efaa101f637b7f2d5122
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/K3DC5G3SY57URD2GZKODGSD5LS \
| 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: 56c62e9b72c77f488f46ca9c33487d5c99dd81a50f43efaa101f637b7f2d5122
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "1d604840388e90e54aa1660879a7b3a1bf61adbd348e9cfac7bbc861fcb90cbc",
"cross_cats_sorted": [
"cs.AI",
"cs.CY",
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2025-08-31T08:36:51Z",
"title_canon_sha256": "a5ac17d4b5b980409f3e4197f20577f2a314531aaa7fd4f892cbc5b6027f39e8"
},
"schema_version": "1.0",
"source": {
"id": "2509.00745",
"kind": "arxiv",
"version": 1
}
}