Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Fast Bayesian gravitational wave parameter estimation using convolutional neural networks
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2309.04303.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T22:21:35.382240Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T19:46:14.910766Z
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 5c0b37fa-5180-410f-b193-c9c58d6c6bf7 · inbound
Learning from galactic rotation curves: a neural network approach cites this paper.
Learning from galactic rotation curves: a neural network approach Fast Bayesian gravitational wave parameter estimation using convolutional neural networks
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:21:35.382240Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1ddc0d3c-b09e-4240-9df7-f1589c5ec171 · inbound
Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective cites this paper.
Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective Fast Bayesian gravitational wave parameter estimation using convolutional neural networks
Reference 270
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:46:14.916242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.