Pith Number
pith:2X3UJRVP
pith:2022:2X3UJRVPKHFB5BXYQWI4F6JZAN
not attested
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not stored
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Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods
arxiv:2201.07931 v1 · 2022-01-20 · cs.CV · eess.IV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{2X3UJRVPKHFB5BXYQWI4F6JZAN}
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Record completeness
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Bitcoin timestamp
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4
Citations
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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 | 2026-07-05T03:50:06.749356Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d5f744c6af51ca1e86f88591c2f9390354da473d2cbae97bc6660c43cb6e4988
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/2X3UJRVPKHFB5BXYQWI4F6JZAN \
| 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: d5f744c6af51ca1e86f88591c2f9390354da473d2cbae97bc6660c43cb6e4988
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "cd0f5c0b8423cf4f061b11dcf4b17b6bf694e2727de71236a7dcc00e7f7c64d7",
"cross_cats_sorted": [
"eess.IV"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2022-01-20T00:50:41Z",
"title_canon_sha256": "86adb7c8e5c72fba34ea9fdc3bce28d5c91af1d4b8eb6662958c8e5649a3f785"
},
"schema_version": "1.0",
"source": {
"id": "2201.07931",
"kind": "arxiv",
"version": 1
}
}