Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2202.08671.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T05:30:31.172422Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T05:30:32.855748Z
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 30e1b261-2190-4c83-903b-83b94ce35b54 · inbound
Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms cites this paper.
Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients
Reference 40
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T05:30:32.861494Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.