{"id":"W2594829375","doi":"10.1287/inte.2016.0864","title":"A Review of Scheduling Problems and Research Opportunities in Motion Picture Exhibition","year":2017,"lang":"en","type":"review","venue":"INFORMS Journal on Applied Analytics","topic":"Cinema and Media Studies","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Exhibition; Movie theater; Film industry; Scheduling (production processes); Computer science; Context (archaeology); Scale (ratio); Engineering; Data science; Multimedia; Visual arts; Art; Operations management; History; Geography","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":[0.00128386,0.001200521,0.001419778,0.00246251,0.0004955062,0.001614205,0.001172753,0.001672544,0.004664534],"category_scores_gemma":[0.003758066,0.0006396354,0.001032123,0.007197314,0.0005897074,0.002511996,0.0005839964,0.001428986,0.001271821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001108517,"about_ca_system_score_gemma":0.00236931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003605595,"about_ca_topic_score_gemma":0.00343424,"domain_scores_codex":[0.9994618,0.0001301825,0.00007688128,0.0001207042,0.0001616748,0.00004876211],"domain_scores_gemma":[0.9970902,0.002233965,0.0001908666,0.00005069756,0.0003650812,0.00006916455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008280088,0.0001865262,0.0009564983,0.02859487,0.0001791989,0.0001888142,0.0001463167,0.01177568,0.0006138115,0.03404198,0.05100156,0.872232],"study_design_scores_gemma":[0.00003183175,0.0002396627,0.003356118,0.01477718,0.0003075176,0.0008892227,0.0004144951,0.006039069,0.0005771732,0.03802431,0.9352465,0.0000969134],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003766888,0.9949922,0.001714204,0.0005478688,0.0002233446,0.000006287218,0.0000378473,0.0000105512,0.002091008],"genre_scores_gemma":[0.003481894,0.9939622,0.001269077,0.000173572,0.0005237934,0.000009824237,0.00007552504,0.000005894387,0.0004981355],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004664534,"threshold_uncertainty_score":0.01560444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4224786817422227,"score_gpt":0.3940462467131995,"score_spread":0.02843243502902321,"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."}}