Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2203.03048.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-14T08:02:00.306457Z
A source-named dated measurement, never combined with another source.
Source: cited_works
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 2017b4c5-6966-4393-94f6-a93675e998cd · inbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws cites this paper.
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T08:02:00.306457Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.