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Paper Citation Record · LEDGER

Photometric Redshift Estimation with a Convolutional Neural Network: NetZ

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2011.12312.

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

pith.paper-citation-record.v1
2011.12312 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:41:58.512413Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T05:10:54.048687Z

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 e233bdcc-418b-45f4-8e44-e989852c2d7d · inbound

SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy: I. Architecture and Automated Redshift Measurement cites this paper.

SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy: I. Architecture and Automated Redshift Measurement Photometric Redshift Estimation with a Convolutional Neural Network: NetZ

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-10T22:41:58.512413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:41:58.512413Z digest=sha256:589e4262dd840c256c52170d7141781af31c5ca8383d1b3b0cfdd94edfd8b34d

Observation ef72c19e-96bc-45c0-9284-e624de711fe8 · inbound

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts cites this paper.

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts Photometric Redshift Estimation with a Convolutional Neural Network: NetZ

Reference 91

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:10:54.050385Z

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.

source=pdf_text observed=2026-05-11T00:51:26.783868Z digest=sha256:ad84cb96ee8bd93fd8573c1e805195032b708e1c31ad915568ddcda9945fc322

Observation 0462d651-7220-4c11-a685-c7759e4c5555 · inbound

SuperMIGHTEE : Spectral Ages of Remnant Radio Galaxy Candidates in the XMM-LSS Field cites this paper.

SuperMIGHTEE : Spectral Ages of Remnant Radio Galaxy Candidates in the XMM-LSS Field Photometric Redshift Estimation with a Convolutional Neural Network: NetZ

Reference 224

Resolution
unresolved
no resolver link, observed 2026-07-14T14:36:35.016368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:36:35.016368Z digest=sha256:01e4f7e7aed9e159a3f956dae6a8208275f4c9e00095b3d7c0fdac2abd930856