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Paper Citation Record · LEDGER

GPT-who: An Information Density-based Machine-Generated Text Detector

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.06202.

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

pith.paper-citation-record.v1
2310.06202 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 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:17:36.636047Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T15:17:36.694885Z

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 73321a76-a6fc-4cc1-bd5f-2e3bf55d3d3a · inbound

Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction cites this paper.

Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction GPT-who: An Information Density-based Machine-Generated Text Detector

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:17:36.701651Z

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.

source=arxiv_source observed=2026-08-11T15:17:36.636047Z digest=sha256:322f270302a85e8502efaae6ee6374c392e6e36558b83a6e246d1a4a2666e1cf

Observation 7406c751-36c6-43c6-98f5-861d1bb99641 · inbound

Using Large Language Models for Idea Generation in Innovation cites this paper.

Using Large Language Models for Idea Generation in Innovation GPT-who: An Information Density-based Machine-Generated Text Detector

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-01T05:52:34.759156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T05:52:34.759156Z digest=sha256:df607e14224b503b6889a5334483b1d801b639f951871b5e5604247915edd8f1