Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural network
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2111.06597.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-09T23:32:28.932487Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-09T23:36:37.061899Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
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- 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 347ba203-7760-49f2-b65b-00c266bb984c · inbound
Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM cites this paper.
Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural network
Reference 65
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T23:36:37.063514Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.