{"id":"W3092059796","doi":"10.1007/s10479-020-03804-4","title":"Early box office prediction in China’s film market based on a stacking fusion model","year":2020,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Cinema and Media Studies","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Gradient boosting; Stacking; Computer science; Random forest; Boosting (machine learning); Artificial intelligence; Box office; Investment (military); Machine learning; Econometrics; Data mining; Economics; Business; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001050223,0.00007586449,0.0002103701,0.0004025057,0.0001556659,0.00004874888,0.0001392073,0.00005441685,0.0004126876],"category_scores_gemma":[0.0009639107,0.00008187833,0.00004999997,0.0005588116,0.00004869484,0.0001779755,0.00006483473,0.0002552558,0.00009787609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002411152,"about_ca_system_score_gemma":0.0000678246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005455289,"about_ca_topic_score_gemma":0.0001373074,"domain_scores_codex":[0.9989288,0.00004699317,0.000378652,0.0002749853,0.0001261156,0.0002443831],"domain_scores_gemma":[0.9994763,0.00007285716,0.00003213738,0.0001878021,0.0001499296,0.0000810454],"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":[0.0008822155,0.0009838091,0.05475799,0.0002195368,0.00007184441,0.00001390124,0.01207043,0.768771,0.001145883,0.03354969,0.1250187,0.002514965],"study_design_scores_gemma":[0.0003865287,0.0002955188,0.1073502,0.00003401978,7.036373e-7,7.440924e-8,0.0001333597,0.8889666,0.00026939,0.0002671945,0.002225882,0.00007059234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9164079,0.0004592892,0.002369026,0.02339181,0.00006005742,0.0004675016,0.0003951786,0.00001895496,0.05643031],"genre_scores_gemma":[0.9972493,0.0004160424,0.0003499506,0.0003583658,0.00006137626,0.00005735769,0.0000249527,0.00001153198,0.00147109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1227928,"threshold_uncertainty_score":0.451864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2255844233831955,"score_gpt":0.3631559994353164,"score_spread":0.1375715760521209,"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."}}