Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:40:36.981638Z
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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.
Pith citing papers
No inbound Pith citation observations are available.