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Paper Citation Record · LEDGER

Discovering Black Hole Mass Scaling Relations with Symbolic Regression

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.19406.

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

pith.paper-citation-record.v1
2310.19406 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:27:34.669468Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T09:21:21.103849Z

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 1daa1edc-1f2e-45c0-bf35-deceeb16dab0 · 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 Discovering Black Hole Mass Scaling Relations with Symbolic Regression

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-16T10:27:34.669468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:27:34.669468Z digest=sha256:7243f41d853f967e5dde4b2c8b265f1d02cf79414871f47b9a4742cb144d66b6

Observation 693dd6c0-7118-4b7a-8716-d7e838d160e7 · 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 Discovering Black Hole Mass Scaling Relations with Symbolic Regression

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-05-22T09:21:21.106662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-22T09:20:09.976842Z digest=sha256:4199db2650aebf73c387298bcbf463fc48894451717b615440343598a546b887