{"id":"W4220896903","doi":"10.1145/3507356","title":"Leveraging Narrative to Generate Movie Script","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thomson Reuters (Canada); Université de Montréal","funders":"","keywords":"Computer science; Narrative; Scripting language; Context (archaeology); Upload; Unavailability; Task (project management); Construct (python library); Information retrieval; Artificial intelligence; Natural language processing; World Wide Web; Linguistics; Programming language","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":[0.0004380701,0.0001137545,0.0001243635,0.0003498933,0.0008329534,0.0003055413,0.0008160637,0.00002469323,0.00004227373],"category_scores_gemma":[0.00001782222,0.0001205425,0.00005025924,0.0005707319,0.000005850156,0.001653195,0.00004020074,0.0002066836,0.0001736993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002405512,"about_ca_system_score_gemma":0.00008111993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001295426,"about_ca_topic_score_gemma":0.000001346582,"domain_scores_codex":[0.9985786,0.0001036981,0.0004612139,0.0001735021,0.0004822507,0.0002007534],"domain_scores_gemma":[0.9989158,0.00004417719,0.000115803,0.0007287355,0.0001057355,0.00008973457],"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":[0.000005326107,0.00001384869,0.000003977233,0.00001277976,0.00001552115,9.273529e-7,0.03556314,0.928872,0.00007604283,0.003392885,0.0005708378,0.03147274],"study_design_scores_gemma":[0.0005243809,0.0001495455,0.00003286188,0.00002451943,0.000005833677,0.00006829999,0.01749197,0.8058042,0.0009011509,0.0002558308,0.1743734,0.0003680305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00897415,0.00001704894,0.9854305,0.001461518,0.002254057,0.0003824739,0.00001472852,0.000234011,0.001231518],"genre_scores_gemma":[0.9821658,0.000001675315,0.01527241,0.001613174,0.00003380532,0.0003975384,0.000006988006,0.000005558796,0.0005030064],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9731917,"threshold_uncertainty_score":0.6406488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03357747367188199,"score_gpt":0.2455136343496178,"score_spread":0.2119361606777358,"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."}}