{"id":"W3198750786","doi":"","title":"Malayalam three-way rhotics contrast: Articulatory modelling based on MRI data","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Malayalam; Contrast (vision); Computer science; Artificial intelligence; Speech recognition; Natural language processing","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":[0.0002984429,0.0003937882,0.000276942,0.0004925273,0.000193563,0.0007613363,0.000344977,0.0005850912,0.002680037],"category_scores_gemma":[0.001213668,0.000164224,0.0005333905,0.0004007702,0.0001974128,0.0003634575,0.0003520545,0.0005609679,0.001642436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001655888,"about_ca_system_score_gemma":0.0004904783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006204528,"about_ca_topic_score_gemma":0.00866243,"domain_scores_codex":[0.9999127,0.00002063377,0.000004988537,0.00001705256,0.00002837688,0.0000162274],"domain_scores_gemma":[0.9997029,0.0001336743,0.00002553382,0.00003684602,0.00007992497,0.00002112297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001686077,0.0002066957,0.00711314,0.0005762157,0.0002054435,0.001489905,0.0005371628,0.2788485,0.308019,0.006659935,0.003213355,0.3914446],"study_design_scores_gemma":[0.00002028929,0.0001274535,0.01296651,0.00004037358,0.0000734529,0.0005661607,0.00008411375,0.9317945,0.05033306,0.0008698935,0.003071292,0.00005279495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4727913,0.0006168985,0.5133439,0.0003419791,0.0001119151,0.0001065582,0.001217811,0.001555777,0.00991377],"genre_scores_gemma":[0.9094886,0.0004826481,0.08201145,0.00004602852,0.000032,0.00005127111,0.001027058,0.0003250295,0.006535932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006204528,"threshold_uncertainty_score":0.01233679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0532952881399329,"score_gpt":0.2398904879386189,"score_spread":0.186595199798686,"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."}}