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Paper Citation Record · LEDGER

Can Bayesian Neural Networks Explicitly Model Input Uncertainty?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2501.08285.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.08285 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:49:29.083031Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T17:21:08.674289Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cec8cbc8-5948-4f2f-a3b1-2426fd12703b · inbound

CNN-Derived Elemental Abundances of LAMOST DR10 Giants: Implications for Galactic Substructures cites this paper.

CNN-Derived Elemental Abundances of LAMOST DR10 Giants: Implications for Galactic Substructures Can Bayesian Neural Networks Explicitly Model Input Uncertainty?

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T23:49:29.083031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:49:29.083031Z digest=sha256:12de2a578abead359f4ac791f827af79eb2385b537904d0700a0a53d28802495

Observation 315540f5-a3e2-4e0c-9b61-b29061823fee · inbound

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems cites this paper.

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems Can Bayesian Neural Networks Explicitly Model Input Uncertainty?

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:21:08.678702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T17:40:50.204175Z digest=sha256:d9c84cc3a9e77d7b2f8b003faaf9e06eceb444f9c747e482a9e43f21ee2b4bbd

Observation 5585f749-57d3-49db-8d30-6f499bca4c22 · inbound

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems cites this paper.

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems Can Bayesian Neural Networks Explicitly Model Input Uncertainty?

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:55:57.466845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-11T02:05:53.638212Z digest=sha256:4d63d07979b56865c9cd68a8183e210c2ac9de930a9eee458a88baf76ed9b967