Pith Number
pith:C3MX2TKW
pith:2026:C3MX2TKWL5DMZXG6IMXXNZCAYG
not attested
not anchored
not stored
refs pending
Can Large Language Models Reliably Code Qualitative Humanitarian Data? A Benchmark Study Against Human Expert Adjudication
arxiv:2606.26541 v1 · 2026-06-25 · cs.LG · cs.CY
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{C3MX2TKWL5DMZXG6IMXXNZCAYG}
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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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 | 2026-06-26T01:15:34.068326Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
16d97d4d565f46ccdcde432f76e440c1b71c302d63c9380e46b93f1d00ac7c07
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/C3MX2TKWL5DMZXG6IMXXNZCAYG \
| 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: 16d97d4d565f46ccdcde432f76e440c1b71c302d63c9380e46b93f1d00ac7c07
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "c29956a086b392888db83f2d2c8271137cf722b43c9ee7b5457285db07c02dd8",
"cross_cats_sorted": [
"cs.CY"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2026-06-25T02:27:23Z",
"title_canon_sha256": "a7edadac9374653e191040ed9d4685aff00c7913c9d98752e7d0dd43520ba3cc"
},
"schema_version": "1.0",
"source": {
"id": "2606.26541",
"kind": "arxiv",
"version": 1
}
}