Paper Citation Record · LEDGER

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2002.06470.

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

pith.paper-citation-record.v1
2002.06470 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:56:12.713017Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:37:25.920402Z

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 1dc0e85c-f58d-4f0b-83fe-6644a4a2d9c5 · inbound

Learning, fast and slow: a two-fold algorithm for data-based model adaptation cites this paper.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:12.713017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8539277-2c5c-4d95-b795-5cbd3fc1c754 · inbound

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking cites this paper.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T11:57:01.195234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4203d8d-b34d-437c-865e-58470ef67477 · inbound

Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification cites this paper.

Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:52.394569Z

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 dd27abc0-9b89-478f-aa4a-7fdddac9a4cd · inbound

Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation cites this paper.

Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T22:37:25.922041Z

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.