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Paper Citation Record · LEDGER

From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2304.03368.

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

pith.paper-citation-record.v1
2304.03368 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:46:00.161827Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

3
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation dbfc381e-422e-4a78-ad37-23d49b18ed58 · inbound

An AI-Based Public Health Data Monitoring System cites this paper.

An AI-Based Public Health Data Monitoring System From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T10:46:00.161827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:46:00.161827Z digest=sha256:c6cfba14d5d5ad9f974c5d48e7f3e436f3098311ef2869018112124cf638554a

Observation bf3b41c4-df16-452a-baa2-894bfdc4a3ea · inbound

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection cites this paper.

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management

Reference 15

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T20:46:22.510725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T20:46:16.567259Z digest=sha256:bc240f4db17266c7ab8d29d3e7fbe4928692e1416ba25b6f746106708ebd7db8