Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep learning approaches for nuclear binding energy prediction: a comparative study of RNN, GRU and LSTM Models
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2503.19348.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-27T22:31:56.701275Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T16:37:09.373929Z
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 ea2026ce-6b79-419a-8cd0-39ef94c4e917 · inbound
Product units in gated recurrent units improve nuclear-mass prediction cites this paper.
Product units in gated recurrent units improve nuclear-mass prediction Deep learning approaches for nuclear binding energy prediction: a comparative study of RNN, GRU and LSTM Models
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:37:09.375367Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.