Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2103.12104.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:29:36.910698Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T14:39:58.449417Z
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 4a9bd7dc-46ca-4279-bdaa-5b6ce97071f4 · inbound
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run cites this paper.
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
Reference 91
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T07:45:53.416718Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 15eb77cc-0902-4497-bd7f-1a06423a275e · inbound
PINCH: Pipeline-Informed Noise Characterization in LIGO's Third Observing Run cites this paper.
PINCH: Pipeline-Informed Noise Characterization in LIGO's Third Observing Run Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:29:36.910698Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2244520c-7c3e-4f28-bf25-9fb22bc82ce8 · inbound
Hunting for new glitches in LIGO data using community science cites this paper.
Hunting for new glitches in LIGO data using community science Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T22:31:53.042565Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 38d6b3dd-a7c6-4839-b7da-ed6556a92cf5 · inbound
When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference cites this paper.
When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T22:42:34.592089Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8e9a95a-180c-463d-bb63-f02bc932656d · inbound
Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks cites this paper.
Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
Reference 63
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T14:39:58.451199Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fc6fd195-6d5b-4dc0-97a1-ce61a3f17d4b · inbound
Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks cites this paper.
Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
Reference 63
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T18:23:51.339929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.