Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Employing deep-learning techniques for the conservative-to-primitive recovery in binary neutron star simulations
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.08289.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:28:25.293324Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T07:29:39.528067Z
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 b588c6ad-7c05-4de6-8fc0-1211f0f75d08 · inbound
A "Neural" Riemann solver for Relativistic Hydrodynamics cites this paper.
A "Neural" Riemann solver for Relativistic Hydrodynamics Employing deep-learning techniques for the conservative-to-primitive recovery in binary neutron star simulations
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:28:25.293324Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1758ad42-2fd3-4cc8-8f34-67c8a122fe09 · inbound
Subgrid Modelling for Relativistic Magnetohydrodynamics with Machine Learning cites this paper.
Subgrid Modelling for Relativistic Magnetohydrodynamics with Machine Learning Employing deep-learning techniques for the conservative-to-primitive recovery in binary neutron star simulations
Reference 79
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T07:29:39.529589Z
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.