Pith Number
pith:EKS4YBUX
pith:2025:EKS4YBUXMGOAQVLKXSB3JWQQU4
not attested
not anchored
not stored
refs pending
Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components
arxiv:2512.08683 v1 · 2025-12-09 · physics.chem-ph
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{EKS4YBUXMGOAQVLKXSB3JWQQU4}
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
✓
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 | 2026-05-20T00:00:29.843790Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
22a5cc0697619c08556abc83b4da10a733e578036f44f5a138d924bb7b4ecf62
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/EKS4YBUXMGOAQVLKXSB3JWQQU4 \
| 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: 22a5cc0697619c08556abc83b4da10a733e578036f44f5a138d924bb7b4ecf62
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "f8213a39d52c3b83009b5ade65586a3442e7fd8a22a28a700079a0ea89e62529",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "physics.chem-ph",
"submitted_at": "2025-12-09T15:06:01Z",
"title_canon_sha256": "7b565e577e6e6fa15b4216e3638416f27ce1479b2aafe9e3b2c4b531a7cc944b"
},
"schema_version": "1.0",
"source": {
"id": "2512.08683",
"kind": "arxiv",
"version": 1
}
}