Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:41:28.974325Z
Paper Citation Record · LEDGER
The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
As of 8 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2506.04584.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
7 of 7 outbound references displayed
- verified exact1
- verified fuzzy1
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 01ad9ca3-2f09-406f-9751-ef34ea668aa0 · outbound
This paper cites The field equations of gravity.
The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration The field equations of gravity
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:41:29.359742Z
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 47567f97-407c-4f35-af49-f386da3b97b7 · outbound
This paper cites Properties of the Binary Black Hole Merger GW150914,.
The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration Properties of the Binary Black Hole Merger GW150914,
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:41:28.508505Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5bc72a39-d654-4722-be3a-6cde62d2061d · outbound
This paper cites Observation of gravitational waves from a binary black hole merger,.
The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration Observation of gravitational waves from a binary black hole merger,
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:41:28.578107Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8fae9d15-71c0-4b5e-adc7-b7931864b5e8 · outbound
This paper cites GW170817: Observation of gravitational waves from a binary neutron star inspiral,.
The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration GW170817: Observation of gravitational waves from a binary neutron star inspiral,
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:41:28.664338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b1f21f22-7adc-426a-9305-b84d3a18a7fb · outbound
This paper cites Deep learning,.
The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration Deep learning,
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:41:28.780404Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb76ad5f-8191-4abd-82eb-39d974d1b8a4 · outbound
This paper cites Deep neural networks to enable real -time low-latency gravitational wave detection,.
The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration Deep neural networks to enable real -time low-latency gravitational wave detection,
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:41:28.870903Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43f95d00-7baf-463e-b49c-334c2931441c · outbound
This paper cites Machine learning for gravitational wave detection and parameter estimation,.
The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration Machine learning for gravitational wave detection and parameter estimation,
Reference 7
- Resolution
- verified exact
- doi, observed 2026-08-07T10:41:29.147134Z
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.
Pith citing papers
No inbound Pith citation observations are available.