Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2303.05986.
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 2026-08-16T00:45:53.066762Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T05:30:32.604844Z
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 c1757531-792c-4fda-b110-1f6f485b2fe1 · inbound
Applications of machine learning in gravitational wave research with current interferometric detectors cites this paper.
Applications of machine learning in gravitational wave research with current interferometric detectors An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
Reference 87
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:42:49.852082Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a0be23c9-31c9-4d23-8d94-60dc0bbe4fb9 · inbound
Can Transformers help us perform parameter estimation of overlapping signals in gravitational wave detectors? cites this paper.
Can Transformers help us perform parameter estimation of overlapping signals in gravitational wave detectors? An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:45:53.066762Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 806d4d3d-9fe6-4a96-a4da-2b6558ebf560 · inbound
Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms cites this paper.
Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
Reference 53
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T05:30:32.613143Z
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.