Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1306.1238.
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 2026-07-13T04:41:12.165957Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-06-26T16:19:35.573110Z
Reference resolution
0 of 0 outbound references displayed
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- malformed identifier0
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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 36c96dd8-7d16-4741-b511-9063f19d91e2 · inbound
LEGGOS I: The JWST LEGGOS Survey -- LEnsing and Galaxy Growth: Observing Substructures -- Unpacks the Nature of Clumpy Star Formation and Quenching in Gravitationally Lensed Galaxies beyond Cosmic Noon cites this paper.
LEGGOS I: The JWST LEGGOS Survey -- LEnsing and Galaxy Growth: Observing Substructures -- Unpacks the Nature of Clumpy Star Formation and Quenching in Gravitationally Lensed Galaxies beyond Cosmic Noon New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift
Reference 211
- Resolution
- verified exact
- local_arxiv, observed 2026-06-26T16:19:35.574301Z
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.
Observation a5f41f02-fda7-4003-ad02-6d57c1ddeb66 · inbound
Performance of morphological classifiers for galaxy mergers compared to current machine learning methods cites this paper.
Performance of morphological classifiers for galaxy mergers compared to current machine learning methods New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift
Reference 90
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:41:12.165957Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.