Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T18:05:32.345440Z
Paper Citation Record · LEDGER
Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters
As of 16 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 1 inbound Pith citation observation for arXiv:2512.07074.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-28T00:28:42.520230Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-02T14:27:03.673948Z
Reference resolution
2 of 2 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 4b8e91ae-d13b-4aae-9314-f508a46f135b · outbound
This paper cites an unresolved cited work.
Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters Unresolved cited work
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T18:05:32.260821Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8720339a-650d-429d-968a-414568a162bd · outbound
This paper cites an unresolved cited work.
Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters Unresolved cited work
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T18:05:32.345440Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 73309fb5-0b83-4945-a8bf-249baaa6b55c · inbound
Reweighting Adversarial Networks for Unbinned Unfolding cites this paper.
Reweighting Adversarial Networks for Unbinned Unfolding Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters
Reference 114
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T14:27:03.675475Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.