{"id":"W3003413888","doi":"10.64152/10125/44715","title":"Synthetic voices in the foreign language context","year":2020,"lang":"en","type":"article","venue":"Language learning & technology","topic":"Digital Communication and Language","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Linguistics; Context (archaeology); Computer science; Foreign language; Comprehension approach; Natural language processing; Psychology; Natural language; History; Philosophy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001036335,0.0003066109,0.0002782593,0.0002414585,0.000239116,0.0007599012,0.0002590799,0.0003701958,0.001763834],"category_scores_gemma":[0.005662163,0.00008939271,0.0002182229,0.0001355148,0.000507586,0.0006821845,0.0009962725,0.000187897,0.0002758621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001770636,"about_ca_system_score_gemma":0.0001550368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004078059,"about_ca_topic_score_gemma":0.0006358966,"domain_scores_codex":[0.9990805,0.0005165231,0.00006481095,0.0001268346,0.0001593035,0.00005194027],"domain_scores_gemma":[0.997736,0.001448862,0.0001901832,0.0001433397,0.0003778902,0.0001037483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003002911,0.0004008427,0.0330686,0.001280204,0.000107484,0.002532057,0.03692403,0.01118039,0.7752565,0.002892364,0.0005896419,0.1327651],"study_design_scores_gemma":[0.0006759197,0.03062708,0.2102219,0.0005859245,0.0005915131,0.01371973,0.07912204,0.0814836,0.5093012,0.009577922,0.06369564,0.0003973296],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912848,0.0001875167,0.006550311,0.00003233054,0.00002077945,0.00004605336,0.00005582579,0.00003763217,0.001784592],"genre_scores_gemma":[0.9954554,0.00007506977,0.003617216,0.0000224232,0.00001424634,0.00003116858,0.00008431665,0.00001255817,0.0006876267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001763834,"threshold_uncertainty_score":0.005900562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144512703063636,"score_gpt":0.2456681955818697,"score_spread":0.2342230685512334,"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."}}