Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2309.00195.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T13:51:43.230416Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T15:16:10.916425Z
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 3f583680-fec0-4a21-9faf-319232e59ed8 · inbound
Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials cites this paper.
Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T13:51:43.230416Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7a051b8-d330-45c5-9683-8b194ba8a780 · 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 On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T15:40:31.637788Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4803eda1-abc7-452d-a7f7-22cdf31ce818 · inbound
Knowing when to trust machine-learned interatomic potentials cites this paper.
Knowing when to trust machine-learned interatomic potentials On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:16:10.920227Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.