Paper Citation Record · LEDGER

ANNz: estimating photometric redshifts using artificial neural networks

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:astro-ph/0311058.

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

pith.paper-citation-record.v1
astro-ph/0311058 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:12:01.593459Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T18:37:31.089521Z

Reference resolution

0 of 0 outbound references displayed

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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 fb1838ff-3607-46ef-b337-0527f1ee55c8 · inbound

The Gravitational Wave Bias Parameter from Angular Power Spectra: Bridging Between Galaxies and Binary Black Holes cites this paper.

The Gravitational Wave Bias Parameter from Angular Power Spectra: Bridging Between Galaxies and Binary Black Holes ANNz: estimating photometric redshifts using artificial neural networks

Reference 106

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:01.593459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ea51fba-c187-42e3-ae33-312f2fc76c34 · inbound

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

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts ANNz: estimating photometric redshifts using artificial neural networks

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:06:04.990929Z

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.

Observation 321d9855-14b8-4c79-8226-2c1b467c127e · inbound

pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches cites this paper.

pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches ANNz: estimating photometric redshifts using artificial neural networks

Reference 254

Resolution: verified exact; local_arxiv, observed 2026-06-27T13:40:58.227773Z

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.

Observation 0b633b78-4f8d-4f63-bc07-9a19650c4bd6 · inbound

Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics cites this paper.

Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics ANNz: estimating photometric redshifts using artificial neural networks

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-06-26T00:58:48.076614Z

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.

Observation e700e133-0f07-475e-94f5-bc6b9027dd04 · inbound

pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web cites this paper.

pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web ANNz: estimating photometric redshifts using artificial neural networks

Reference 256

Resolution: verified exact; local_arxiv, observed 2026-06-30T01:04:08.691072Z

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.

Observation abe3df59-a418-4cff-a4ec-796bab67b0f4 · inbound

Infrared-enhanced Photometric Redshifts for the Dark Energy Survey Y6 Gold catalogue cites this paper.

Infrared-enhanced Photometric Redshifts for the Dark Energy Survey Y6 Gold catalogue ANNz: estimating photometric redshifts using artificial neural networks

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-10T18:37:31.090922Z

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.