{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005063307,0.0008901695,0.0003292465,0.002040284,0.01003626,0.01352329,0.0009806027,0.003230753,0.02055629],"category_scores_gemma":[0.007917619,0.0004225866,0.0005134852,0.002937921,0.01878712,0.006208108,0.004493104,0.004442973,0.002692934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0225693,"about_ca_system_score_gemma":0.01856504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3590379,"about_ca_topic_score_gemma":0.5403345,"domain_scores_codex":[0.9947412,0.00219542,0.0001249957,0.0005381505,0.001443157,0.0009570378],"domain_scores_gemma":[0.9955099,0.001658066,0.0004722555,0.0003874781,0.001032944,0.0009394354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001714721,0.00009444977,0.007796117,0.0006007289,0.00007133958,0.00050483,0.1467079,0.0003104269,0.001848931,0.4669151,0.2402218,0.1347569],"study_design_scores_gemma":[0.000008585012,0.00002626568,0.007011379,0.0002361097,0.00001842697,0.00009156953,0.02035755,0.0000385483,0.0005219068,0.004570334,0.9670934,0.00002593772],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0588652,0.04433247,0.00348746,0.1333624,0.003383685,0.0001314326,0.0006836313,0.0001798214,0.7555738],"genre_scores_gemma":[0.6217496,0.02606373,0.00266488,0.01979882,0.0009532424,0.0001486906,0.0002658632,0.0003429616,0.3280123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3590379,"threshold_uncertainty_score":0.7138962,"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."}}