Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Statistical methods for resolving poor uncertainty quantification in machine learning interatomic potentials
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2308.15653.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T15:40:30.116774Z
A source-named dated measurement, never combined with another source.
Source: cited_works
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 77eb0f9d-4ab0-4f8f-b108-42036e18ae96 · inbound
Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials cites this paper.
Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Statistical methods for resolving poor uncertainty quantification in machine learning interatomic potentials
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T15:40:30.116774Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.