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Paper Citation Record · LEDGER

Modeling the galaxy-halo connection with machine learning

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2111.02422.

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

pith.paper-citation-record.v1
2111.02422 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:27:34.658228Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T09:21:21.212797Z

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 3b4d9bf9-5e5c-4f55-bf84-48675c8732c3 · inbound

SymbolFit: Automatic Parametric Modeling with Symbolic Regression cites this paper.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Modeling the galaxy-halo connection with machine learning

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T20:17:48.884996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:17:48.884996Z digest=sha256:3b4aa266062d5082451c8fdc85f224f1b4e763b735880af1b534dfff861f577c

Observation 4f8fbf42-6489-476d-a787-816e79301067 · inbound

Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes cites this paper.

Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes Modeling the galaxy-halo connection with machine learning

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T10:27:34.658228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:27:34.658228Z digest=sha256:361f9838e065e6c7b1e3ab41b066d445bfd807cbc4e2cffcc52bf1a6f39293f0

Observation cc36bb76-0b21-4626-a60f-43c238c3989c · inbound

Cosmological Simulations of Galaxies cites this paper.

Cosmological Simulations of Galaxies Modeling the galaxy-halo connection with machine learning

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-06T18:15:59.997672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:15:59.997672Z digest=sha256:d9c28eaaa29109a4af473c2a6ca98e5cd01bca6863a2ae72234379ef3ede5ed5

Observation 30499af7-8513-4e26-a54e-0fd25df4e46a · inbound

Predicting intermediate-mass black hole formation in star clusters with machine learning cites this paper.

Predicting intermediate-mass black hole formation in star clusters with machine learning Modeling the galaxy-halo connection with machine learning

Reference 93

Resolution
verified exact
arxiv_id, observed 2026-05-22T09:21:21.215113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-22T09:20:09.976842Z digest=sha256:d28fd2e054b2b61ef521b5e482bcf4e6aaa62171dc0b56c8e9de444bef28f59c