Paper Citation Record · LEDGER

Detection and classification of radio sources with deep learning

As of 16 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2411.08519.

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

pith.paper-citation-record.v1
2411.08519 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:34:32.362968Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact1
verified fuzzy4
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 09af8338-7780-4ca9-9df0-e103f2d84feb · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Detection and classification of radio sources with deep learning , " * write output.state after.block = add.period write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T21:34:32.312407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8d62bc4-e745-433d-96ca-74d5b18dd306 · outbound

This paper cites write newline.

Detection and classification of radio sources with deep learning write newline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T21:34:32.317530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9135bd5-5022-40e1-8f38-22a8e5020e8f · outbound

This paper cites Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection.

Detection and classification of radio sources with deep learning Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T21:34:32.321950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57e0e676-ddda-4fa5-bb53-5b7867fd492c · outbound

This paper cites 2020, in Proc.

Detection and classification of radio sources with deep learning 2020, in Proc

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:34:32.545402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0ae4287f-02dd-4b2b-b827-812d306b8b69 · outbound

This paper cites 2009, in 2009 IEEE conference on computer vision and pattern recognition, 248.

Detection and classification of radio sources with deep learning 2009, in 2009 IEEE conference on computer vision and pattern recognition, 248

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:34:32.532855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 833f2557-3a62-4c8c-bdfb-b15304f09393 · outbound

This paper cites 2017, in 2017 IEEE International Conference on Computer Vision (ICCV), 2980.

Detection and classification of radio sources with deep learning 2017, in 2017 IEEE International Conference on Computer Vision (ICCV), 2980

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:34:32.519998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59b1fc27-c5d6-43c6-9db1-9038ee6a13dc · outbound

This paper cites Australian Square Kilometre Array Pathfinder: I. System Description.

Detection and classification of radio sources with deep learning Australian Square Kilometre Array Pathfinder: I. System Description

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T21:34:32.339598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a1a7faa-7ca7-44ad-ad66-158626d19b83 · outbound

This paper cites 2023, IEEE Transactions on Knowledge & Data Engineering, 35, 857.

Detection and classification of radio sources with deep learning 2023, IEEE Transactions on Knowledge & Data Engineering, 35, 857

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:34:32.505069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9c320065-23af-461d-924b-fc89dbbc4eb2 · outbound

This paper cites EMU: Evolutionary Map of the Universe.

Detection and classification of radio sources with deep learning EMU: Evolutionary Map of the Universe

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T21:34:32.348345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61fade22-cbef-441f-beb1-edd17f704f1b · outbound

This paper cites Astronomical source detection in radio continuum maps with deep neural networks.

Detection and classification of radio sources with deep learning Astronomical source detection in radio continuum maps with deep neural networks

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T21:34:32.444269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1da39abf-93b9-4fa1-bef5-2a0a6ed72f93 · outbound

This paper cites RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation.

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.

Observation a84fd2a7-69f1-45a8-948f-9f28b6233125 · outbound

This paper cites Radio astronomical images object detection and segmentation: A benchmark on deep learning methods.

Detection and classification of radio sources with deep learning Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:34:32.409318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.