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Paper Citation Record · LEDGER

Nuclear energy density functionals from machine learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2105.07696.

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

pith.paper-citation-record.v1
2105.07696 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:23:36.558267Z

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

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  • 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 d2625843-905f-48bd-a6a0-aebb9a257e70 · 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 Nuclear energy density functionals from machine learning

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-02T03:23:36.558267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:23:36.558267Z digest=sha256:f2572717bc8b5fad0ccb82e74d5350732a2b6ffd3ee412063b3a2a8e2f20bab3

Observation 12f31bb2-dec4-4ae0-aa42-1f3fdac9fcbc · inbound

Self-consistent orbital-free nuclear density functional theory with a physics-constrained learned nonlocal kinetic energy functional cites this paper.

Self-consistent orbital-free nuclear density functional theory with a physics-constrained learned nonlocal kinetic energy functional Nuclear energy density functionals from machine learning

Reference 17

Resolution
unresolved
no resolver link, observed 2026-07-31T23:51:48.220342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T23:51:48.220342Z digest=sha256:b770f5caf1676085287df323a8ea46d29dd7c98a261226cad8e359982c7fbccf