Paper Citation Record · LEDGER

Modeling assembly bias with machine learning and symbolic regression

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2012.00111.

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

pith.paper-citation-record.v1
2012.00111 v1

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 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:52:39.960155Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1298
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b2d977a4-1d34-45ab-9e55-416e8d99e973 · inbound

Predicting Halo Formation Time Using Machine Learning cites this paper.

Predicting Halo Formation Time Using Machine Learning Modeling assembly bias with machine learning and symbolic regression

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:39.960155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d673c29-406c-409f-8494-739e61578194 · 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 assembly bias with machine learning and symbolic regression

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T10:27:34.649811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b13b347-334a-410c-a2da-9f72734db36a · inbound

Effect Sizes in Marketing Research: Why Cohen's Local f^2 Belongs in the Toolkit cites this paper.

Effect Sizes in Marketing Research: Why Cohen's Local f^2 Belongs in the Toolkit Modeling assembly bias with machine learning and symbolic regression

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:49:31.127128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b665ccdc-891c-4835-88b3-46caf3fb8234 · inbound

HI Simulations for Cosmology with the SKA Observatory cites this paper.

HI Simulations for Cosmology with the SKA Observatory Modeling assembly bias with machine learning and symbolic regression

Reference 79

Resolution: metadata mismatch; arxiv_id, observed 2026-06-25T19:58:18.173168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.