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Paper Citation Record · LEDGER

RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2307.02392.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2307.02392 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:34:32.358392Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-12T18:16:22.223912Z

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 1da39abf-93b9-4fa1-bef5-2a0a6ed72f93 · inbound

Detection and classification of radio sources with deep learning cites this paper.

Detection and classification of radio sources with deep learning RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T21:34:32.358392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:34:32.358392Z digest=sha256:6ca04d1bc5dd896f26ec935e221ff59506b0717840689d01daac7bdfbb798e39

Observation 5e344acc-8519-4c7d-b9c1-17dfd5146301 · inbound

Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation cites this paper.

Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation

Reference 78

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:15:30.192188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-12T14:15:29.654590Z digest=sha256:f9ecdb259f021f96d6de09f19647b4129993a624b40a9a2b8552873a333ad9ac