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Paper Citation Record · LEDGER

Mapping Machine-Learned Physics into a Human-Readable Space

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2010.11998.

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

pith.paper-citation-record.v1
2010.11998 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:29:11.630871Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T08:29:11.040064Z

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 15ab0b21-3bb5-4344-be79-cef66afa93cd · inbound

Optimizing The Cut And Count Method In Phenomenological Studies cites this paper.

Optimizing The Cut And Count Method In Phenomenological Studies Mapping Machine-Learned Physics into a Human-Readable Space

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-24T08:29:11.043525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-24T08:28:28.093588Z digest=sha256:b7053c07814e49191cd94da2bd9e85acd0910270e92a673347ab617005062bbf

Observation 54bbbdce-72fa-488b-bfd2-63d12e7c23e9 · inbound

Theory-informed neural networks for particle physics cites this paper.

Theory-informed neural networks for particle physics Mapping Machine-Learned Physics into a Human-Readable Space

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:29:11.630871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:29:11.630871Z digest=sha256:a3befdca47750e41fe593abde59090996963f07e7e76fe8c3f64da4af05b1221