Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2302.02005.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T11:24:17.067906Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T11:24:17.608217Z
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 f9ec0878-1f7c-49ce-9ba3-0a3d6d99825d · inbound
From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption cites this paper.
From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
Reference 61
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T11:24:17.613477Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 90c7a4c4-d30d-4fb9-a66e-43aa1d9cad66 · 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 DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:22:28.094441Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.