{"id":"W2107540152","doi":"10.1509/jmkg.64.3.18.18026","title":"Sales through Sequential Distribution Channels: An Application to Movies and Videos","year":2000,"lang":"en","type":"article","venue":"Journal of Marketing","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Renting; Distributor; Channel (broadcasting); Advertising; Attendance; Movie theater; Perspective (graphical); Computer science; Business; Product (mathematics); Marketing; Economics; Telecommunications; Mathematics; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002476883,0.0009362137,0.001316576,0.001352605,0.001259229,0.00188231,0.001410633,0.001956271,0.01149244],"category_scores_gemma":[0.01176358,0.0009047355,0.001632224,0.002542488,0.001259319,0.002495612,0.001548995,0.002188188,0.000602686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002641252,"about_ca_system_score_gemma":0.001896403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02347097,"about_ca_topic_score_gemma":0.02062177,"domain_scores_codex":[0.9988804,0.0004977313,0.0000321023,0.0001960435,0.0002179744,0.0001755542],"domain_scores_gemma":[0.9861166,0.01211261,0.0006355202,0.0003732007,0.0004702376,0.0002918327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006574584,0.0007979131,0.01233975,0.0002107502,0.00009817644,0.0006367111,0.0004538542,0.8187187,0.001669938,0.07635427,0.003028245,0.08503427],"study_design_scores_gemma":[0.00008011532,0.0002451318,0.002855901,0.00002514204,0.00005505116,0.0001695949,0.0002052116,0.9650226,0.0007726716,0.0281485,0.00236729,0.00005285507],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4538582,0.002003462,0.5159307,0.002421994,0.0001991537,0.0005779756,0.0007233602,0.0007183849,0.02356686],"genre_scores_gemma":[0.8717039,0.001585115,0.1173156,0.0001337363,0.0001388679,0.0002332889,0.0002648779,0.0001476441,0.00847703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02347097,"threshold_uncertainty_score":0.04666871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925932520065721,"score_gpt":0.2611950739339166,"score_spread":0.2419357487332593,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}