Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1303.6984.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T10:33:50.882513Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T10:33:51.011609Z
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 101b4b85-3e19-475a-a48a-6fa41dc88183 · inbound
SGNAX: a unified matched-filter and excess-power pipeline for gravitational-wave detector characterization cites this paper.
SGNAX: a unified matched-filter and excess-power pipeline for gravitational-wave detector characterization Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T10:33:51.021658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.