Pith Number
pith:7UPHP6YK
pith:2019:7UPHP6YKT7XNTLEZ45GZ3OFLXC
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refs pending
Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?
arxiv:1909.05501 v2 · 2019-09-12 · q-fin.RM · stat.AP · stat.ML
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{7UPHP6YKT7XNTLEZ45GZ3OFLXC}
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Record completeness
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Bitcoin timestamp
2
Internet Archive
3
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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.
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Receipt and verification
| First computed | 2026-07-05T00:12:53.437239Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
fd1e77fb0a9feed9ac99e74d9db8abb8823fdb2e1c06994ea37bb4e3d67baf6c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/7UPHP6YKT7XNTLEZ45GZ3OFLXC \
| 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: fd1e77fb0a9feed9ac99e74d9db8abb8823fdb2e1c06994ea37bb4e3d67baf6c
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "1411d1cd8abe27a3d49b52827d4056a1ae2941d69ee73da2e887ac128311709a",
"cross_cats_sorted": [
"stat.AP",
"stat.ML"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "q-fin.RM",
"submitted_at": "2019-09-12T08:22:56Z",
"title_canon_sha256": "74a2970cb4219ac927d6039847f0fa719d1e9b4d348ae8bb3dc3d6c2599aca72"
},
"schema_version": "1.0",
"source": {
"id": "1909.05501",
"kind": "arxiv",
"version": 2
}
}