{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002295507,0.0004613274,0.0004435737,0.003206392,0.001340541,0.006184717,0.001296771,0.001564773,0.006906428],"category_scores_gemma":[0.01077552,0.0003385591,0.0004475449,0.002524272,0.0008071347,0.004243704,0.001721562,0.0008892908,0.00178516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002593487,"about_ca_system_score_gemma":0.00156394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06560244,"about_ca_topic_score_gemma":0.06087855,"domain_scores_codex":[0.9978951,0.0009141606,0.000144801,0.0005376767,0.0003581426,0.0001500216],"domain_scores_gemma":[0.9906095,0.005087516,0.0003377091,0.001003229,0.002419365,0.0005427105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003023033,0.000663028,0.07034113,0.001048828,0.0002869642,0.003545822,0.06337237,0.01219738,0.02701481,0.1399209,0.05317531,0.6254104],"study_design_scores_gemma":[0.0002619962,0.0008017703,0.09861033,0.0007844628,0.0003475692,0.004855353,0.0721987,0.09498674,0.04156207,0.05939626,0.6256375,0.0005572524],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5703117,0.005350224,0.2913469,0.009798989,0.0007392768,0.0004923077,0.04861336,0.008374305,0.06497291],"genre_scores_gemma":[0.8335415,0.001525223,0.1203935,0.0005722127,0.000182804,0.000351827,0.02482173,0.0007404417,0.01787068],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.06560244,"threshold_uncertainty_score":0.1304412,"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."}}