{"id":"W2914209400","doi":"10.1038/s41598-019-38612-9","title":"Machine learning for MEG during speech tasks","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; SickKids Foundation; Vector Institute; Public Safety Canada; Hospital for Sick Children; St. Michael's Hospital; Centre for Social Innovation","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Speech recognition; Natural language processing; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0008187204,0.0001357026,0.0001672989,0.0001473172,0.0004017511,0.0004462496,0.0002349028,0.00004477864,0.0002013793],"category_scores_gemma":[0.0004038363,0.0001147294,0.0001073423,0.0002979624,0.0001024767,0.000247927,0.000149097,0.0001628195,0.0001442339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002793259,"about_ca_system_score_gemma":0.00003976485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008928087,"about_ca_topic_score_gemma":0.000003675141,"domain_scores_codex":[0.9978122,0.00004705416,0.0003450083,0.0009947197,0.0004141215,0.0003868826],"domain_scores_gemma":[0.998937,0.0001108641,0.0002304407,0.0005776444,0.00005940003,0.00008464557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009439044,0.00002997983,0.005694868,0.00004719339,0.000002689093,0.000163016,0.0002304204,0.0006091878,0.9903358,0.00007088546,0.001304988,0.001501491],"study_design_scores_gemma":[0.0001491604,0.00004097483,0.0003209059,0.00003384853,0.000003683763,0.0005293036,0.00001985448,0.004673589,0.9024598,0.001247979,0.09036291,0.0001579926],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846804,0.0000463459,0.0002273281,0.0001279671,0.009818295,0.0004268796,0.000002285805,0.0001615463,0.004508979],"genre_scores_gemma":[0.9464417,0.000001221278,0.0004311142,0.00005598388,0.00007942269,0.00001109405,0.000007846906,0.00001848338,0.05295318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08905793,"threshold_uncertainty_score":0.4678529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02234467053725263,"score_gpt":0.2692482728448178,"score_spread":0.2469036023075651,"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."}}