Pith Number
pith:Q6SPMCEK
pith:2019:Q6SPMCEKQVNBANKUADWD3HWCN5
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refs pending
Deep learning methods based on cross-section images for predicting effective thermal conductivity of composites
arxiv:1904.06104 v1 · 2019-04-12 · physics.comp-ph · physics.data-an
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{Q6SPMCEKQVNBANKUADWD3HWCN5}
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Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
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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 | 2026-05-17T23:48:44.113591Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
87a4f6088a855a10355400ec3d9ec26f71a6ce6b6de5cf8fe14535195f8216d5
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Q6SPMCEKQVNBANKUADWD3HWCN5 \
| 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: 87a4f6088a855a10355400ec3d9ec26f71a6ce6b6de5cf8fe14535195f8216d5
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "a76b3839a1e1ca2e6dc7620d421edc8f71b2db40ff2c26157636e3e265c72478",
"cross_cats_sorted": [
"physics.data-an"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "physics.comp-ph",
"submitted_at": "2019-04-12T08:50:57Z",
"title_canon_sha256": "27b52278799d12791bd2609d91a38c68b9d4a521c105048daf022ee6171e9d30"
},
"schema_version": "1.0",
"source": {
"id": "1904.06104",
"kind": "arxiv",
"version": 1
}
}