Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Evaluating Cosmological Biases using Photometric Redshifts for Type Ia Supernova Cosmology with the Dark Energy Survey Supernova Program
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.16744.
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 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T00:11:20.518635Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T21:26:51.875808Z
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 f2c8a5ed-369b-4a28-b799-15178d28a503 · inbound
CIGaRS I: Combined simulation-based inference from type Ia supernovae and host photometry cites this paper.
CIGaRS I: Combined simulation-based inference from type Ia supernovae and host photometry Evaluating Cosmological Biases using Photometric Redshifts for Type Ia Supernova Cosmology with the Dark Energy Survey Supernova Program
Reference 74
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:26:51.879072Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 74088af8-3c21-4493-ad29-7202180bc5ae · inbound
Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey cites this paper.
Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Evaluating Cosmological Biases using Photometric Redshifts for Type Ia Supernova Cosmology with the Dark Energy Survey Supernova Program
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T00:11:20.518635Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.