Pith Number
pith:IFH3MFE4
pith:2025:IFH3MFE4KKX64HT2OEVJXKG2G6
not attested
not anchored
not stored
refs pending
Data-Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data
arxiv:2504.07007 v1 · 2025-04-09 · cond-mat.mtrl-sci
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{IFH3MFE4KKX64HT2OEVJXKG2G6}
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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claim
4
Citations
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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 | 2026-07-05T10:46:49.780070Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
414fb6149c52afee1e7a712a9ba8da3793d4fd13e4cd8eb5b6a75d26b974352b
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/IFH3MFE4KKX64HT2OEVJXKG2G6 \
| 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: 414fb6149c52afee1e7a712a9ba8da3793d4fd13e4cd8eb5b6a75d26b974352b
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "b3ad3ef7f8deba2a9f32c3684a5dfbf2b4cdeb0b05c3545dedbdd20cd31ce347",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cond-mat.mtrl-sci",
"submitted_at": "2025-04-09T16:26:21Z",
"title_canon_sha256": "90894e8b724d868da120191a31867ee13459532393c062485572243c07a8fc06"
},
"schema_version": "1.0",
"source": {
"id": "2504.07007",
"kind": "arxiv",
"version": 1
}
}