Paper Citation Record · LEDGER

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2501.03282.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.03282 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:13:15.561451Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

7
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7415efe0-6a36-4fd4-ba96-0d97f8295df3 · inbound

Epistemic Uncertainty in Conformal Scores: A Unified Approach cites this paper.

Epistemic Uncertainty in Conformal Scores: A Unified Approach From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-08T14:13:15.561451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2b546f5-54f6-4345-86cf-bebba7efbaba · inbound

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications cites this paper.

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T22:23:17.131524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfcc4eaf-86b7-4f34-933e-425b606503ae · inbound

Localising Dropout Variance in Twin Networks cites this paper.

Localising Dropout Variance in Twin Networks From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:02:07.780018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a0a9c76b-1813-4ab0-98a6-dbe99f2c6b12 · inbound

Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification cites this paper.

Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Reference 75

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T14:05:29.159724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d129a758-557a-48eb-9b86-526d34a70c6a · inbound

A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification cites this paper.

A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:49:10.719829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c6ae76c-3187-4070-9ba7-5e12185f3182 · inbound

Methods for Uncertainty Representation in Risk Management: A Comparative Review and Decision-Oriented Framework cites this paper.

Methods for Uncertainty Representation in Risk Management: A Comparative Review and Decision-Oriented Framework From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Reference 167

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T02:23:00.621059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6db3a26d-1edb-4142-b304-119250381b72 · inbound

Polarization-Conditioned Fourier-enhanced DeepONet for Electric Field Reconstruction from EFISH Measurements cites this paper.

Polarization-Conditioned Fourier-enhanced DeepONet for Electric Field Reconstruction from EFISH Measurements From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T20:49:59.550503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.