{"as_of":"2026-08-08T15:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:131522f1d15adc3076598fee18bed5064326f151e870e9c9e6517951d2e4a310","coverage":[{"denominator":8,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:40:36.981638Z","state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.00076/citation-record","integrity":"/paper/2506.00076/integrity","json":"/paper/2506.00076/citation-record.json","paper":"/paper/2506.00076"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:40:38.491054Z","title":"(2024, January)","venue":null,"work_id":"46204320-d420-4202-bfc2-4a06eed9cb20","year":2024},"citing_paper":{"arxiv_id":"2506.00076","last_updated":"2025-05-29T23:01:54Z","snapshot_observed_at":"2026-08-07T12:34:27.627317Z","submitted_at":"2025-05-29T23:01:54Z","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:36.323943Z"},"links":{"citing_paper":"/paper/2506.00076"},"observation_digest":"sha256:7bcaf6423322e03ff449ee4549e25a59c472e40761fe822dce742b79649ae8c0","observation_id":"ef2f83c1-cab3-4f67-983a-cc914b37b2b3","resolution":{"observed_at":"2026-08-07T12:40:38.572101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:40:38.281770Z","title":"Forecasting television viewership: A machine learning approach using narrative content and metadata,","venue":null,"work_id":"0c9e3a70-6020-4913-9b97-99340f94667f","year":2022},"citing_paper":{"arxiv_id":"2506.00076","last_updated":"2025-05-29T23:01:54Z","snapshot_observed_at":"2026-08-07T12:34:27.627317Z","submitted_at":"2025-05-29T23:01:54Z","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:36.391955Z"},"links":{"citing_paper":"/paper/2506.00076"},"observation_digest":"sha256:ad8381e282469aa3123840413f7dd899757e923764e77c1e4850842ed0586153","observation_id":"32445139-74fc-45d1-8867-595f782aa48a","resolution":{"observed_at":"2026-08-07T12:40:38.349312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"literature/1140126","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:40:37.420050Z","title":"Prediction of TV program ratings based on machine learning models,","venue":null,"work_id":"97cc1d13-af4b-4d2b-af10-78d7cdc3b573","year":2021},"citing_paper":{"arxiv_id":"2506.00076","last_updated":"2025-05-29T23:01:54Z","snapshot_observed_at":"2026-08-07T12:34:27.627317Z","submitted_at":"2025-05-29T23:01:54Z","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:36.497103Z"},"links":{"citing_paper":"/paper/2506.00076"},"observation_digest":"sha256:3b0af87a8bf5a1544bfe5afbddf8e756e3770c977c893613df6c978825b360e3","observation_id":"fbcf5aac-5ce7-46df-842a-7c5c9960b1f9","resolution":{"observed_at":"2026-08-07T12:40:37.470976Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:40:38.133897Z","title":null,"venue":null,"work_id":"07cfb30d-2ef7-48b4-8794-0c23f21a9b50","year":null},"citing_paper":{"arxiv_id":"2506.00076","last_updated":"2025-05-29T23:01:54Z","snapshot_observed_at":"2026-08-07T12:34:27.627317Z","submitted_at":"2025-05-29T23:01:54Z","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:36.569075Z"},"links":{"citing_paper":"/paper/2506.00076"},"observation_digest":"sha256:f7d6e98f519c06e79b521708e16b2fc3859b3eb91779b9375bbe078d3edfcc4c","observation_id":"86c5903f-7341-45b6-938b-0d37e55b88c2","resolution":{"observed_at":"2026-08-07T12:40:38.210669Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:40:37.932133Z","title":"Using machine learning to predict future TV ratings in an evolving media landscape,","venue":null,"work_id":"d6bf3415-00a5-46f0-9224-ddd458501837","year":2016},"citing_paper":{"arxiv_id":"2506.00076","last_updated":"2025-05-29T23:01:54Z","snapshot_observed_at":"2026-08-07T12:34:27.627317Z","submitted_at":"2025-05-29T23:01:54Z","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:36.656424Z"},"links":{"citing_paper":"/paper/2506.00076"},"observation_digest":"sha256:4205eb1e6337dff139ab86a770ae6bb3ec58f26b7dc46f778df8246c71ab71b6","observation_id":"b2190971-4a9d-436f-9181-03ec2dec5d46","resolution":{"observed_at":"2026-08-07T12:40:38.043739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"edu/8592612","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:40:37.201400Z","title":"Predicting Nielsen ratings from pilot episodes’ scripts: A content analytical approach,","venue":null,"work_id":"1ef8f663-1807-4efc-b350-b0885d2aab95","year":null},"citing_paper":{"arxiv_id":"2506.00076","last_updated":"2025-05-29T23:01:54Z","snapshot_observed_at":"2026-08-07T12:34:27.627317Z","submitted_at":"2025-05-29T23:01:54Z","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:36.800327Z"},"links":{"citing_paper":"/paper/2506.00076"},"observation_digest":"sha256:f41fd9bbff0cdd2f5128d2bd9ca18b99774410c8c2407a8cbefeccb57afe9e95","observation_id":"e299be2e-79a6-4cce-9483-d2392fdf7d0e","resolution":{"observed_at":"2026-08-07T12:40:37.283031Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:40:37.743529Z","title":"The SARIMAX model,","venue":null,"work_id":"f6be5bef-ca8e-4636-928b-457de4f6fe21","year":2021},"citing_paper":{"arxiv_id":"2506.00076","last_updated":"2025-05-29T23:01:54Z","snapshot_observed_at":"2026-08-07T12:34:27.627317Z","submitted_at":"2025-05-29T23:01:54Z","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:36.897152Z"},"links":{"citing_paper":"/paper/2506.00076"},"observation_digest":"sha256:6773ec3b4e6b42b33f309d593b449f043ebdf44a6369467fc91fb47197f389f7","observation_id":"925276b2-998d-42da-8867-fd4ec1d54407","resolution":{"observed_at":"2026-08-07T12:40:37.828880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:40:37.560171Z","title":"Explaining XGBoost predictions with SHAP value: A comprehensive guide to interpreting decision tree-based models,","venue":null,"work_id":"03bd4ca6-afb1-4cb1-9c88-474241abb6f5","year":2023},"citing_paper":{"arxiv_id":"2506.00076","last_updated":"2025-05-29T23:01:54Z","snapshot_observed_at":"2026-08-07T12:34:27.627317Z","submitted_at":"2025-05-29T23:01:54Z","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:36.981638Z"},"links":{"citing_paper":"/paper/2506.00076"},"observation_digest":"sha256:4ff9612afce1c31461d32078c67b6b769906f31da38e230cd23843f8bba3c7df","observation_id":"fa31217a-e919-47ee-a380-16ad3cabb21c","resolution":{"observed_at":"2026-08-07T12:40:37.649306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.00076","last_updated":"2025-05-29T23:01:54Z","latest_version":1,"primary_category":"cs.CY","snapshot_observed_at":"2026-08-07T12:34:27.627317Z","submitted_at":"2025-05-29T23:01:54Z","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry"},"reference_resolution":{"displayed":8,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":2,"verified_fuzzy":5},"total_outbound_references":8},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"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."}