Pith Number
pith:7N4MIBOJ
pith:2024:7N4MIBOJUFJSFNDLILKDYYBC7N
not attested
not anchored
not stored
refs pending
Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts
arxiv:2404.15419 v4 · 2024-04-23 · physics.ao-ph · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{7N4MIBOJUFJSFNDLILKDYYBC7N}
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 | 2026-07-05T09:17:12.865464Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
fb78c405c9a15322b46b42d43c6022fb6ba13010c2dea6f17fd5e290855d83a4
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/7N4MIBOJUFJSFNDLILKDYYBC7N \
| 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: fb78c405c9a15322b46b42d43c6022fb6ba13010c2dea6f17fd5e290855d83a4
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "8734c27dcd5cf5a8238ec4461df8ab68a7cb898757d2070310802d1504b64951",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "physics.ao-ph",
"submitted_at": "2024-04-23T18:10:18Z",
"title_canon_sha256": "9675feed71935995c98001a98b6ade753217b0b0983163df82e2b7ecb9381f0f"
},
"schema_version": "1.0",
"source": {
"id": "2404.15419",
"kind": "arxiv",
"version": 4
}
}