Pith Number
pith:FMMBUWOH
pith:2024:FMMBUWOHBZYZHQXYYWVUPHA6NP
not attested
not anchored
not stored
refs pending
Recurrent Neural Networks for Modelling Gross Primary Production
arxiv:2404.12745 v1 · 2024-04-19 · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{FMMBUWOHBZYZHQXYYWVUPHA6NP}
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 | 2026-07-05T09:12:27.435187Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2b181a59c70e7193c2f8c5ab479c1e6bdef7f43e1bc4aa297e0d3522c793c53c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FMMBUWOHBZYZHQXYYWVUPHA6NP \
| 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: 2b181a59c70e7193c2f8c5ab479c1e6bdef7f43e1bc4aa297e0d3522c793c53c
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "dbd6ecdf8d8194e97d8f29bb6d5863b9ac1585247109193d5c0149b2697ba58c",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2024-04-19T09:46:45Z",
"title_canon_sha256": "66324aeee2ee1193074b6386cade8e7f22270dc40f8970b1b79722176602d29b"
},
"schema_version": "1.0",
"source": {
"id": "2404.12745",
"kind": "arxiv",
"version": 1
}
}