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Paper Citation Record · LEDGER

From NS observations to nuclear matter properties: a machine learning approach

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.05770.

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

pith.paper-citation-record.v1
2401.05770 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 2026-08-08T23:14:27.560436Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T15:39:05.225873Z

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 83b383ad-3b1f-4762-b545-f8c5cfb053dd · inbound

Exploring the limits of nucleonic metamodelling using different relativistic density functionals cites this paper.

Exploring the limits of nucleonic metamodelling using different relativistic density functionals From NS observations to nuclear matter properties: a machine learning approach

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-08T23:14:27.560436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T23:14:27.560436Z digest=sha256:16ff5ef7efd04cc6ac2871ce30b7bd2a2121e7ddd81ac1b8026a44c167e7c3bd

Observation ea71914f-49d3-4b9d-baf2-69c9dbd26a21 · inbound

Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data cites this paper.

Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data From NS observations to nuclear matter properties: a machine learning approach

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-05T15:39:05.229638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T15:39:05.017722Z digest=sha256:9e77123fb287c217a5ff1df0f246c4666e995b2ea8b94a304e0c71937ec5ceb0