Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
RAPID: Early Classification of Explosive Transients using Deep Learning
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1904.00014.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:22:35.579342Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-11T06:57:51.608216Z
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 5a8ded4a-78a3-436a-aa61-6f8d376df0da · inbound
From stellar light to astrophysical insight: automating variable star research with machine learning cites this paper.
From stellar light to astrophysical insight: automating variable star research with machine learning RAPID: Early Classification of Explosive Transients using Deep Learning
Reference 128
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:22:35.579342Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8214953a-4bad-4ad9-aaba-18a53f6abf1e · inbound
High Power Accretion in Massive Binary Systems and the Impact of Metallicity cites this paper.
High Power Accretion in Massive Binary Systems and the Impact of Metallicity RAPID: Early Classification of Explosive Transients using Deep Learning
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T18:26:36.471816Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 856469fc-c206-41a3-9631-5619cc9069e1 · inbound
Uncertainty-Aware Deep Learning for the Ly$\alpha$ Forest: CNN-Based Absorber Detection and Characterization cites this paper.
Uncertainty-Aware Deep Learning for the Ly$\alpha$ Forest: CNN-Based Absorber Detection and Characterization RAPID: Early Classification of Explosive Transients using Deep Learning
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-07-11T06:57:51.610246Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.