Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving Photometric Redshift Estimation for Cosmology with LSST using Bayesian Neural Networks
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.13179.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T23:36:13.173755Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T21:02:25.853061Z
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 ecabd3c4-c39e-442a-8468-b16dac5d5dc0 · inbound
Estimating Photometric Redshifts for Galaxies from the DESI Legacy Imaging Surveys with Bayesian Neural Networks Trained by DESI EDR cites this paper.
Estimating Photometric Redshifts for Galaxies from the DESI Legacy Imaging Surveys with Bayesian Neural Networks Trained by DESI EDR Improving Photometric Redshift Estimation for Cosmology with LSST using Bayesian Neural Networks
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:36:13.173755Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 882be9d2-e089-4a16-b1cf-f9082e89661f · inbound
Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs) cites this paper.
Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs) Improving Photometric Redshift Estimation for Cosmology with LSST using Bayesian Neural Networks
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T21:02:25.859650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.