Paper Citation Record · LEDGER

Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach

As of 21 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 1 inbound Pith citation observation for arXiv:2501.12723.

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

pith.paper-citation-record.v1
2501.12723 v2

Coverage vector

measured 3 of 3 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:56:46.743764Z

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:32:31.229677Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:32:33.648068Z

Reference resolution

3 of 3 outbound references displayed

verified exact1
verified fuzzy1
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fb45b01d-6964-4b3f-8112-db73f15492c9 · outbound

This paper cites Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations.

Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T16:56:46.793651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5bdfdb79-01ef-4223-a557-8940ffa2f3f1 · outbound

This paper cites X., Gutierrez-Portela, F., Moreno Hernandez, J.

Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach X., Gutierrez-Portela, F., Moreno Hernandez, J

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:56:46.803438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bdbb43a8-d5a4-4d91-acda-78db830d0941 · outbound

This paper cites Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks.

Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks

Reference 450

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:56:46.779231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation cf5010a6-b0b4-4c35-89f9-a329e8dc4833 · inbound

We Need to Rethink Benchmarking in Anomaly Detection cites this paper.

We Need to Rethink Benchmarking in Anomaly Detection Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:32:33.651539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.