Paper Citation Record · LEDGER

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry

As of 8 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 0 inbound Pith citation observations for arXiv:2506.00076.

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

pith.paper-citation-record.v1
2506.00076 v1

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:40:36.981638Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

8 of 8 outbound references displayed

verified exact2
verified fuzzy5
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ef2f83c1-cab3-4f67-983a-cc914b37b2b3 · outbound

This paper cites (2024, January).

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry (2024, January)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:38.572101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 32445139-74fc-45d1-8867-595f782aa48a · outbound

This paper cites Forecasting television viewership: A machine learning approach using narrative content and metadata,.

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry Forecasting television viewership: A machine learning approach using narrative content and metadata,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:38.349312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fbcf5aac-5ce7-46df-842a-7c5c9960b1f9 · outbound

This paper cites Prediction of TV program ratings based on machine learning models,.

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry Prediction of TV program ratings based on machine learning models,

Reference 3

Resolution: verified exact; raw_fallback, observed 2026-08-07T12:40:37.470976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 86c5903f-7341-45b6-938b-0d37e55b88c2 · outbound

This paper cites an unresolved cited work.

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:40:38.210669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b2190971-4a9d-436f-9181-03ec2dec5d46 · outbound

This paper cites Using machine learning to predict future TV ratings in an evolving media landscape,.

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry Using machine learning to predict future TV ratings in an evolving media landscape,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:38.043739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e299be2e-79a6-4cce-9483-d2392fdf7d0e · outbound

This paper cites Predicting Nielsen ratings from pilot episodes’ scripts: A content analytical approach,.

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry Predicting Nielsen ratings from pilot episodes’ scripts: A content analytical approach,

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-07T12:40:37.283031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 925276b2-998d-42da-8867-fd4ec1d54407 · outbound

This paper cites The SARIMAX model,.

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry The SARIMAX model,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:37.828880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fa31217a-e919-47ee-a380-16ad3cabb21c · outbound

This paper cites Explaining XGBoost predictions with SHAP value: A comprehensive guide to interpreting decision tree-based models,.

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry Explaining XGBoost predictions with SHAP value: A comprehensive guide to interpreting decision tree-based models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:37.649306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.