Pith Number
pith:LBZN2DDZ
pith:2017:LBZN2DDZLMS2NBVXTFTT4QJ6IZ
not attested
not anchored
not stored
refs pending
Identifying Coherent Anomalies in Multi-Scale Spatio-Temporal Data using Markov Random Fields
arxiv:1711.00395 v1 · 2017-10-18 · stat.AP
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{LBZN2DDZLMS2NBVXTFTT4QJ6IZ}
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.
Receipt and verification
| First computed | 2026-05-18T00:31:32.209163Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5872dd0c795b25a686b799673e413e464c14e3a49c6cc53f07a7804d5de44230
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LBZN2DDZLMS2NBVXTFTT4QJ6IZ \
| 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: 5872dd0c795b25a686b799673e413e464c14e3a49c6cc53f07a7804d5de44230
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "84f554c9ed9fc047ffb7415f33585b70aa3dd7665caba5c13b8ef46411aa6b1a",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "stat.AP",
"submitted_at": "2017-10-18T10:30:00Z",
"title_canon_sha256": "02f768222966a46faafe169effae3881610ae9284df08c6868d78ce90233305e"
},
"schema_version": "1.0",
"source": {
"id": "1711.00395",
"kind": "arxiv",
"version": 1
}
}