{"id":"W2920748761","doi":"10.4000/1895.6558","title":"« Truquer, créer, innover. Les effets spéciaux français »","year":2018,"lang":"fr","type":"article","venue":"1895","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Art; Movie theater; Art history","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001669827,0.0003517653,0.0003179896,0.00008426735,0.0003892271,0.001623441,0.0007212664,0.0002440136,0.0007625639],"category_scores_gemma":[0.0001899445,0.000273274,0.0001575242,0.0007496474,0.0006071674,0.003005351,0.0004636034,0.0002492776,0.004646727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006687266,"about_ca_system_score_gemma":0.0001297193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005048532,"about_ca_topic_score_gemma":0.0009973554,"domain_scores_codex":[0.997844,0.00007346024,0.0003704377,0.0005692131,0.0004087116,0.0007342239],"domain_scores_gemma":[0.998324,0.0001110328,0.000128081,0.0006712761,0.0003991207,0.0003664745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000325419,0.0003234866,0.0004051966,0.00006241608,0.00005706479,0.0002689323,0.005761831,0.000003296996,0.0008829303,0.3253874,0.3612454,0.3055694],"study_design_scores_gemma":[0.0006586838,0.0007194091,0.01254504,0.0001948038,0.0000195178,0.0002480374,0.00003441211,0.001690212,0.005852986,0.01667703,0.960854,0.0005058526],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1478086,0.01760946,0.01516132,0.01190219,0.01158161,0.0003793425,0.00006880225,0.0003698237,0.7951188],"genre_scores_gemma":[0.8871259,0.0001557168,0.002250895,0.00307616,0.002328639,0.000004827352,0.000009328997,0.00002725891,0.1050213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7393173,"threshold_uncertainty_score":0.9999719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2370748657571801,"score_gpt":0.3295875883167993,"score_spread":0.09251272255961915,"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."}}