Paper Citation Record · LEDGER

Enhancing Gravitational-Wave Science with Machine Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2005.03745.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2005.03745 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T15:18:34.550100Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T15:21:44.932068Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 8b4ea3ca-dcf8-43f5-a1fb-6d91371b5455 · inbound

Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data cites this paper.

Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data Enhancing Gravitational-Wave Science with Machine Learning

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-22T15:21:44.935034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 41dd1d33-3e24-4151-aa5b-417e462a390d · inbound

Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework cites this paper.

Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework Enhancing Gravitational-Wave Science with Machine Learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:10:38.314004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e13543c-45a2-44b6-8b16-9afd132fbc06 · inbound

Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection cites this paper.

Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection Enhancing Gravitational-Wave Science with Machine Learning

Reference 123

Resolution: verified exact; arxiv_id, observed 2026-05-21T03:33:56.198340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1da35553-f9c1-4b25-ac7e-9a9ab11406e0 · inbound

Predicting intermediate-mass black hole formation in star clusters with machine learning cites this paper.

Predicting intermediate-mass black hole formation in star clusters with machine learning Enhancing Gravitational-Wave Science with Machine Learning

Reference 96

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T09:21:21.233041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.