Pith Number
pith:WN4RH7IC
pith:2013:WN4RH7ICCAC6ZAA2L56JW2SGNN
not attested
not anchored
not stored
refs pending
Qualitative detection of oil adulteration with machine learning approaches
arxiv:1305.3149 v1 · 2013-05-14 · cs.CE · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{WN4RH7ICCAC6ZAA2L56JW2SGNN}
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 | 2026-05-18T03:25:44.645930Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b37913fd021005ec801a5f7c9b6a466b72d16e7d7a0a512a9a10e0a91525aa0e
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/WN4RH7ICCAC6ZAA2L56JW2SGNN \
| 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: b37913fd021005ec801a5f7c9b6a466b72d16e7d7a0a512a9a10e0a91525aa0e
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "24c406822ecd19fab836158a9a294d329af01eef03c0f41c877c0a46b5d1cab7",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by-nc-sa/3.0/",
"primary_cat": "cs.CE",
"submitted_at": "2013-05-14T13:23:19Z",
"title_canon_sha256": "2e837e22567d9ca5e14d411a7e0ce4bbf79d2d24ffb53a92a9ad7e625611a6bc"
},
"schema_version": "1.0",
"source": {
"id": "1305.3149",
"kind": "arxiv",
"version": 1
}
}