Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:physics/9701026.
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 2026-08-06T15:27:30.668571Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-03T15:28:34.798126Z
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 e5cf59da-e79e-4637-8561-894e65f211ab · inbound
Semantic-Aware Gaussian Process Calibration with Structured Layerwise Kernels for Deep Neural Networks cites this paper.
Semantic-Aware Gaussian Process Calibration with Structured Layerwise Kernels for Deep Neural Networks Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:27:30.668571Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e2458b95-f851-4f2b-bdde-65271fd9d1e8 · inbound
Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration cites this paper.
Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T15:28:34.799433Z
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.