Pith Number
pith:EVFFG7QO
pith:2018:EVFFG7QOGOH7WYHXSFVLL4OWCZ
not attested
not anchored
not stored
refs pending
Applied Machine Learning to Predict Stress Hotspots II: Hexagonal close packed materials
arxiv:1804.05924 v1 · 2018-04-16 · cond-mat.mtrl-sci
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{EVFFG7QOGOH7WYHXSFVLL4OWCZ}
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
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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 | 2026-05-18T00:18:22.352204Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
254a537e0e338ffb60f7916ab5f1d6164447c2dd50eb689b5a0bc85e568fd6f5
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/EVFFG7QOGOH7WYHXSFVLL4OWCZ \
| 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: 254a537e0e338ffb60f7916ab5f1d6164447c2dd50eb689b5a0bc85e568fd6f5
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "83bb53cac79888274757de0122479297562bc5eb559837f8539365c2850c081c",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cond-mat.mtrl-sci",
"submitted_at": "2018-04-16T20:15:06Z",
"title_canon_sha256": "a8fbd9667ef566e6076ec601fbc0e0ea9258a128e9abf0db36297cd2e08a9751"
},
"schema_version": "1.0",
"source": {
"id": "1804.05924",
"kind": "arxiv",
"version": 1
}
}