{"id":"W4246248617","doi":"10.31234/osf.io/jb4wh","title":"Evaluating generalised additive mixed modelling strategies for dynamic speech analysis","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Set (abstract data type); Range (aeronautics); Formant; Focus (optics); Data set; Data mining; Artificial intelligence; Speech recognition","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":[0.0006427867,0.0004019793,0.0007339724,0.0004668515,0.0001654953,0.001016973,0.001253789,0.0002630579,0.0002708468],"category_scores_gemma":[0.0001149393,0.0003856911,0.0008341412,0.0007378925,0.00003247329,0.000308806,0.0006792938,0.0003407818,0.00004440316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001180221,"about_ca_system_score_gemma":0.0004655958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001443577,"about_ca_topic_score_gemma":0.0001485538,"domain_scores_codex":[0.9969606,0.0002133676,0.0005845685,0.001301261,0.000563956,0.0003762678],"domain_scores_gemma":[0.9977244,0.0004796516,0.0003500018,0.0007746927,0.0005111967,0.0001601028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000355042,0.00009422142,0.000004608907,0.0001708789,0.003238396,0.00002669113,0.001118733,0.6055956,0.001137945,0.0189433,0.0009688097,0.3686653],"study_design_scores_gemma":[0.000205321,0.00003658348,0.0000183228,0.00003027973,0.000558062,0.000001405552,0.0002978206,0.9269488,0.002300699,0.06909712,0.00006012537,0.0004454779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009902077,0.00005355836,0.9837164,0.0009834832,0.0004099459,0.0006818978,0.0002224266,0.0004679185,0.003562241],"genre_scores_gemma":[0.174998,0.00003722736,0.8235505,0.0003617233,0.0001082836,0.0002422398,0.0003831747,0.00002359473,0.0002952969],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3682199,"threshold_uncertainty_score":0.9998595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1663628758113608,"score_gpt":0.3671104395742344,"score_spread":0.2007475637628736,"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."}}