Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.15406.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T17:11:04.737610Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T22:47:25.733858Z
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 21732308-da53-4731-a183-e22cc5a69d90 · inbound
Distribution free uncertainty quantification in neuroscience-inspired deep operators cites this paper.
Distribution free uncertainty quantification in neuroscience-inspired deep operators Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T17:11:04.737610Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 12cb26c4-69c1-40c4-b557-bed2b2072675 · inbound
Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime cites this paper.
Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T22:47:25.735199Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.