{"id":"W4388652381","doi":"","title":"Literary Natural Language Generation with Psychological Traits","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Natural language generation; Computer science; Natural (archaeology); Natural language; Natural language processing; Linguistics; Psychology; History; Philosophy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001551393,0.0003194159,0.0003143212,0.0001153381,0.0001987472,0.0007960022,0.002255503,0.0002281119,0.00003439869],"category_scores_gemma":[0.000346453,0.0002848485,0.0001354998,0.0003477079,0.00009129068,0.0002886257,0.001469331,0.0008956476,0.00003139246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006554043,"about_ca_system_score_gemma":0.0001397795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009804218,"about_ca_topic_score_gemma":0.0001638042,"domain_scores_codex":[0.9951256,0.002506964,0.000403918,0.001183185,0.0004727876,0.0003075241],"domain_scores_gemma":[0.9966173,0.0003169628,0.0003013046,0.001869067,0.0007143012,0.0001810682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001967317,0.0005988032,0.0003476945,0.0001817527,0.0001140245,0.0001268559,0.05783876,0.0004164827,0.01817132,0.2928451,0.001832444,0.6275071],"study_design_scores_gemma":[0.0008651267,0.000001804344,0.003172579,0.0009970347,0.00003141452,0.00009841809,0.00007042515,0.9638946,0.02044957,0.007266519,0.002220172,0.0009323357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1028373,0.001603017,0.8634474,0.01763892,0.0002937588,0.0003481422,0.00001427954,0.0005222237,0.01329495],"genre_scores_gemma":[0.6901048,0.00004601063,0.308025,0.0004353602,0.00006671683,0.00003638726,0.0001649532,0.00001899514,0.001101756],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9634781,"threshold_uncertainty_score":0.9999604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0259775689900038,"score_gpt":0.2548634877187326,"score_spread":0.2288859187287288,"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."}}