Paper Citation Record · LEDGER

Rediscovering orbital mechanics with machine learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2202.02306.

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

pith.paper-citation-record.v1
2202.02306 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 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:26:34.825210Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T08:54:15.936031Z

Reference resolution

0 of 0 outbound references displayed

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verified fuzzy0
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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 25adec6b-2785-4703-8295-d44ebc86464e · inbound

Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems cites this paper.

Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems Rediscovering orbital mechanics with machine learning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:54:15.938900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 17092829-eae4-4fb7-a923-b76c4980d42a · inbound

On the definition and importance of interpretability in scientific machine learning cites this paper.

On the definition and importance of interpretability in scientific machine learning Rediscovering orbital mechanics with machine learning

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T14:11:38.671325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b1e89266-5b10-4472-8d54-68025b3e7272 · inbound

$\mathcal{CP}$-Analyses with Symbolic Regression cites this paper.

$\mathcal{CP}$-Analyses with Symbolic Regression Rediscovering orbital mechanics with machine learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T19:26:34.825210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3b0c4bb-71a2-4fb0-88e1-3acd62b157b2 · 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 Rediscovering orbital mechanics with machine learning

Reference 91

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T09:21:21.152886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.