{"id":"W4255429295","doi":"10.32920/ryerson.14644209","title":"Demand driven operations management in motion picture exhibition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"","keywords":"Exhibition; Attendance; Ticket; Computer science; Entertainment industry; Film industry; Loyalty business model; Entertainment; Scheduling (production processes); Loyalty; Operations research; Marketing; Movie theater; Engineering; Business; Operations management; Economics","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":[0.001257745,0.0005543412,0.0005427105,0.0005050342,0.0004832342,0.001792512,0.0007812369,0.000669148,0.00303772],"category_scores_gemma":[0.002123627,0.000368525,0.0003862545,0.001117189,0.0002890415,0.001322914,0.0006778454,0.000796438,0.0003638153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001616427,"about_ca_system_score_gemma":0.001255479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008759421,"about_ca_topic_score_gemma":0.007378213,"domain_scores_codex":[0.9993772,0.0002495791,0.00004330607,0.0001112458,0.0001182309,0.0001005198],"domain_scores_gemma":[0.9990035,0.0005542087,0.0001385041,0.00004425599,0.0001640011,0.0000955768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003528275,0.0002541367,0.004208666,0.0002809492,0.00006573864,0.0002434318,0.0004086547,0.8880684,0.003877682,0.01119411,0.005324856,0.08572058],"study_design_scores_gemma":[0.00002204527,0.0001549063,0.003279767,0.0000284476,0.00001203991,0.00003148061,0.0006143444,0.984086,0.0009581018,0.006840939,0.003942137,0.00002976278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5381998,0.002942102,0.4251338,0.004095383,0.0001561461,0.0006590363,0.001514958,0.0009957041,0.02630302],"genre_scores_gemma":[0.9542056,0.00115115,0.03896633,0.0000951199,0.00005871663,0.0001696428,0.0007186154,0.00004739733,0.004587393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008759421,"threshold_uncertainty_score":0.01741683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07536322665787293,"score_gpt":0.3791287979462312,"score_spread":0.3037655712883582,"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."}}