{"id":"W3001674625","doi":"","title":"Introducing two databases of spoken French throughout adulthood","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Natural language processing; Database; Spoken language; Artificial intelligence; Information retrieval","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":[0.00631944,0.0003959209,0.0006550527,0.0002415039,0.0004122273,0.0006791812,0.005125991,0.0001560534,0.00003852874],"category_scores_gemma":[0.003552966,0.0004049265,0.0002635475,0.0002694479,0.0002655572,0.0004946996,0.005056717,0.0006121985,0.00006256469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009211893,"about_ca_system_score_gemma":0.0006359439,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01456205,"about_ca_topic_score_gemma":0.00335573,"domain_scores_codex":[0.9934614,0.003374378,0.0007673066,0.001265756,0.0006674792,0.0004636725],"domain_scores_gemma":[0.9872088,0.001077832,0.001304002,0.007954226,0.002243757,0.0002113589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002846293,0.001944211,0.0279189,0.001578025,0.0004760408,0.00005447681,0.04865453,0.000467492,0.01135394,0.6160074,0.01410363,0.2774129],"study_design_scores_gemma":[0.005459735,0.000003953485,0.04040351,0.01968574,0.0002688161,0.0001387839,0.0004204781,0.128784,0.6802969,0.05897158,0.06145939,0.004107135],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0296606,0.00223686,0.9101092,0.005034152,0.00150309,0.0006317287,0.0002187148,0.0003668269,0.05023886],"genre_scores_gemma":[0.7128113,0.0002924931,0.284394,0.00004758832,0.0001209147,0.00004874629,0.0004857845,0.0000323516,0.001766867],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6831507,"threshold_uncertainty_score":0.9998403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02491340741050474,"score_gpt":0.2739538464529392,"score_spread":0.2490404390424345,"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."}}