Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Measuring training variability from stochastic optimization using robust nonparametric testing
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.08307.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T20:15:39.733941Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T03:49:29.497421Z
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 f33e9151-a610-48b6-984e-2d2a97ca62fe · inbound
Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation cites this paper.
Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation Measuring training variability from stochastic optimization using robust nonparametric testing
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:15:39.733941Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2b200a1c-7a17-49ca-8dcc-efd6a8e55dbb · inbound
The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation cites this paper.
The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation Measuring training variability from stochastic optimization using robust nonparametric testing
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T03:49:29.499814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.