Pith Number
pith:MFGRZKXN
pith:2019:MFGRZKXNGCJG7QOCHYF3UR46S2
not attested
not anchored
not stored
refs pending
Adaptive Ensemble Learning of Spatiotemporal Processes with Calibrated Predictive Uncertainty: A Bayesian Nonparametric Approach
arxiv:1904.00521 v1 · 2019-04-01 · stat.ME
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{MFGRZKXNGCJG7QOCHYF3UR46S2}
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
· sign in to
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 | 2026-05-17T23:49:48.200459Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
614d1caaed30926fc1c23e0bba479e969a3c84efcfa69ac470996b862b0c514d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/MFGRZKXNGCJG7QOCHYF3UR46S2 \
| 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: 614d1caaed30926fc1c23e0bba479e969a3c84efcfa69ac470996b862b0c514d
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "6d17557f79b9f35096bec42ac904372e7d501db05e9fde9a88746a20c2d48594",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "stat.ME",
"submitted_at": "2019-04-01T01:03:57Z",
"title_canon_sha256": "9eca674d150bfb200eb5baf37ae7225b6f0f97ba1681313add7eb4e50a852c15"
},
"schema_version": "1.0",
"source": {
"id": "1904.00521",
"kind": "arxiv",
"version": 1
}
}