Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2110.06968.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:50:19.505179Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-12T14:50:19.571427Z
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 5072012a-e5a9-4430-8e34-6afcc6b05721 · inbound
Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning cites this paper.
Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
Reference 79
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T14:50:19.576607Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f551d375-4627-4314-89ca-0a0882d71565 · 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 Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
Reference 235
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:42:50.594235Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.