Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Opportunities in Machine Learning for Particle Accelerators
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1811.03172.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:43:09.842065Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T18:16:30.548575Z
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 95903c7c-da21-46ea-872d-e14043fc55aa · inbound
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry cites this paper.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Opportunities in Machine Learning for Particle Accelerators
Reference 2018
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T14:43:11.544386Z
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.
Observation f9e193f5-d798-42cd-af06-36279d8f124b · inbound
Machine Learning for Complex Instrument Design and Optimization cites this paper.
Machine Learning for Complex Instrument Design and Optimization Opportunities in Machine Learning for Particle Accelerators
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T01:37:28.754780Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.