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Paper Citation Record · LEDGER

Evaluating the Performance of ChatGPT for Spam Email Detection

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.15537.

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

pith.paper-citation-record.v1
2402.15537 v3

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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:56:58.016342Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T00:56:58.051442Z

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 1bba7c73-1ba5-4f8c-ae7f-381030891ed4 · inbound

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models cites this paper.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Evaluating the Performance of ChatGPT for Spam Email Detection

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-16T00:56:58.056562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T00:56:58.016342Z digest=sha256:b0944b28f5f90519912f923798d8506e0d1698f4fd6630a47e16ed1d08791792

Observation e289675e-5a2b-4cf6-8ad8-b820980fed37 · inbound

Large Language Models for Security Operations Centers: A Comprehensive Survey cites this paper.

Large Language Models for Security Operations Centers: A Comprehensive Survey Evaluating the Performance of ChatGPT for Spam Email Detection

Reference 279

Resolution
unresolved
no resolver link, observed 2026-08-04T17:32:21.626465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:32:21.626465Z digest=sha256:d8112a162e6fe2c83000d9e80704909e73921d3f5ef0594ab32397c3b1c017f3