Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.01960.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T13:15:42.264049Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T20:32:45.632431Z
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 c32729ab-8b1a-41ff-88f7-60be55043e7f · inbound
Locally Adaptive Conformal Inference for Operator Models cites this paper.
Locally Adaptive Conformal Inference for Operator Models Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
Reference 2009
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:15:42.264049Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6e1b683e-ee8d-4270-b5e4-78b3aa45f954 · inbound
Flow-Based Conformal Predictive Distributions cites this paper.
Flow-Based Conformal Predictive Distributions Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T06:12:25.962040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3a73e5fb-355c-4b47-b46b-2eb699e33e37 · inbound
Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects cites this paper.
Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
Reference 158
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T20:32:45.634131Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2663bce3-940b-4f7f-98e3-d49af7b67e8c · inbound
NeuroForge: A Self-Correcting, Geometry-Native Neural CFD Engine with Calibrated Physics-Residual Trust cites this paper.
NeuroForge: A Self-Correcting, Geometry-Native Neural CFD Engine with Calibrated Physics-Residual Trust Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T12:34:40.550770Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 05e12f67-0fd7-4a0b-ace4-07240ae5e011 · inbound
Multi-Granularity Conformal Prediction for Reliable Neural-Operator Automotive Aerodynamic Surrogates cites this paper.
Multi-Granularity Conformal Prediction for Reliable Neural-Operator Automotive Aerodynamic Surrogates Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T18:29:55.210693Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.