{"id":"W4394904851","doi":"10.1515/9782760645028-015","title":"Remerciements","year":2022,"lang":"fr","type":"book-chapter","venue":"Les Presses de l'Université de Montréal eBooks","topic":"Diverse Cultural and Historical Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Social Sciences and Humanities Research Council","funders":"","keywords":"Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01113177,0.001482501,0.001532605,0.003834984,0.004526637,0.00990249,0.003017268,0.002320797,0.2603539],"category_scores_gemma":[0.06653632,0.0004905091,0.000978481,0.002926958,0.002375,0.005696528,0.007480071,0.005777866,0.150076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009439816,"about_ca_system_score_gemma":0.01686502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008712364,"about_ca_topic_score_gemma":0.01625099,"domain_scores_codex":[0.9801485,0.003730605,0.0009328905,0.002415631,0.01153596,0.001236352],"domain_scores_gemma":[0.927141,0.006231006,0.002008581,0.007786474,0.04504624,0.01178685],"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":[0.00006110607,0.0000431425,0.0005081807,0.0002006499,0.00001073362,0.0003360366,0.003201773,0.00009313173,0.0007181892,0.02749383,0.9049644,0.06236869],"study_design_scores_gemma":[0.000002866581,0.000008794654,0.0002150819,0.0001140243,0.000002978823,0.0001459708,0.0006901029,0.00003108727,0.0001124851,0.001338804,0.997331,0.000006947776],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.007562831,0.01887826,0.01623446,0.2090183,0.1207085,0.0009737347,0.01405809,0.003224783,0.609341],"genre_scores_gemma":[0.02568589,0.004875733,0.008100257,0.01158651,0.008048439,0.0004696538,0.003640209,0.001777471,0.9358158],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7396461,"threshold_uncertainty_score":0.8709704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02838914269343349,"score_gpt":0.205445437227121,"score_spread":0.1770562945336875,"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."}}