Paper Citation Record · LEDGER

Why is the Mahalanobis Distance Effective for Anomaly Detection?

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2003.00402.

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

pith.paper-citation-record.v1
2003.00402 v2

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 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:51:53.159843Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:29:57.956929Z

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 c8f45deb-2fb1-41df-b61e-9149e3023496 · inbound

Non-Linear Outlier Synthesis for Out-of-Distribution Detection cites this paper.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Why is the Mahalanobis Distance Effective for Anomaly Detection?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T16:51:53.159843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffc0d5b8-f545-4414-b0e7-58bc53386570 · inbound

VOLTA: The Surprising Ineffectiveness of Auxiliary Losses for Calibrated Deep Learning cites this paper.

VOLTA: The Surprising Ineffectiveness of Auxiliary Losses for Calibrated Deep Learning Why is the Mahalanobis Distance Effective for Anomaly Detection?

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:36:01.418372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ff54aabf-4612-4a68-8c17-6937ed3b410c · inbound

Mahalanobis-Guided Latent OOD Detection for Hybrid ES-DRL Control in Time-Varying Systems cites this paper.

Mahalanobis-Guided Latent OOD Detection for Hybrid ES-DRL Control in Time-Varying Systems Why is the Mahalanobis Distance Effective for Anomaly Detection?

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:37:37.777509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9a40805c-366e-4271-b97b-98640c0a39ec · inbound

Modality-Aware Out-of-Distribution Detection for Multi-Modal Action Recognition cites this paper.

Modality-Aware Out-of-Distribution Detection for Multi-Modal Action Recognition Why is the Mahalanobis Distance Effective for Anomaly Detection?

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:29:57.958278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.