{"id":"W7008077464","doi":"","title":"Amélioration de la robustesse des systèmes de reconnaissance automatique du locuteur dans l'espace des i-vecteurs","year":2014,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Digital audio broadcasting; Limiting","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002833878,0.001763568,0.0008663248,0.0007952626,0.000977267,0.005851048,0.00178005,0.002641157,0.01149924],"category_scores_gemma":[0.009718962,0.0007651454,0.001228757,0.0005378254,0.003066294,0.003446409,0.003077049,0.002406928,0.003693999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001449268,"about_ca_system_score_gemma":0.001513858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004558956,"about_ca_topic_score_gemma":0.003461072,"domain_scores_codex":[0.9970901,0.0005857524,0.0001403443,0.0008833078,0.00106145,0.0002390111],"domain_scores_gemma":[0.9940677,0.002566005,0.0006984796,0.001027346,0.001455136,0.0001854027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006705372,0.00008886084,0.005395201,0.001242401,0.0001979493,0.0006743406,0.003200199,0.09889369,0.2282332,0.09737597,0.004730695,0.5592969],"study_design_scores_gemma":[0.00009157192,0.00202865,0.02020751,0.000812861,0.0002551085,0.002620354,0.003095117,0.5137494,0.2201637,0.0495555,0.1870123,0.0004080264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05017214,0.002594978,0.908426,0.001130244,0.0004654035,0.000104244,0.0001001121,0.001272762,0.03573409],"genre_scores_gemma":[0.6872326,0.002970019,0.2507567,0.0005879929,0.0004509928,0.0002506787,0.0003608486,0.000604116,0.05678617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01149924,"threshold_uncertainty_score":0.03846872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00298648361010184,"score_gpt":0.1514230445461355,"score_spread":0.1484365609360337,"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."}}