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Paper Citation Record · LEDGER

Machine learning in nuclear physics at low and intermediate energies

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2301.06396.

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

pith.paper-citation-record.v1
2301.06396 v1

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T08:12:16.394008Z

measured 0 of 1 external citation measurements

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

Source: cited_works

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 6a0ed293-5dc1-44f7-97e1-7b0ea32300eb · inbound

Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model cites this paper.

Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model Machine learning in nuclear physics at low and intermediate energies

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-03T08:12:16.394008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:12:16.394008Z digest=sha256:41c9e470c1a7dc5393e66d710fcaed42b29573db509fca2d255a7854320b717f

Observation 698f6b3e-09d6-4bde-8198-cbcc36f1aa29 · inbound

NNStar: An end-to-end AI agent for nuclear matter and neutron star physics cites this paper.

NNStar: An end-to-end AI agent for nuclear matter and neutron star physics Machine learning in nuclear physics at low and intermediate energies

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-02T03:23:37.159563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:23:37.159563Z digest=sha256:8b344f9bc9f89b82c14012bd152d5294bf64ada764b8b85fc94e090a46d30d35