{"id":"W3214734602","doi":"10.1016/j.csl.2021.101322","title":"Empirical Mode Decomposition articulation feature extraction on Parkinson’s Diadochokinesia","year":2021,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"H2020 Marie Skłodowska-Curie Actions; Horizon 2020; Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Universidad de Antioquia","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Hilbert–Huang transform; Frame (networking); Feature (linguistics); Filter (signal processing); Speech recognition; Computer vision; Telecommunications","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":[0.00009216573,0.0001824226,0.0002422189,0.0001046165,0.00007971004,0.00006483129,0.00005911845,0.0001594334,0.0002144382],"category_scores_gemma":[0.00003750814,0.000165283,0.0001500963,0.0002531639,0.00001781046,0.0001358006,0.00003259975,0.0003119031,0.0001629722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001371779,"about_ca_system_score_gemma":0.00007031066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001435333,"about_ca_topic_score_gemma":0.00005499524,"domain_scores_codex":[0.9986937,0.00007064169,0.0001845006,0.0003891541,0.0004177187,0.0002442726],"domain_scores_gemma":[0.9992197,0.00004725634,0.00006040761,0.0003896415,0.0001468795,0.0001360813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005677975,0.002166708,0.03287493,0.0002226898,0.0002481731,0.007510711,0.003602291,0.001289229,0.1231779,0.0002416278,0.106859,0.7212389],"study_design_scores_gemma":[0.004118263,0.0006313955,0.7471077,0.0003471997,0.0002894778,0.002251394,0.0004553843,0.05790489,0.1211782,0.000309017,0.0646885,0.0007186143],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9646677,0.0003900111,0.02411549,0.007075613,0.0004370114,0.0002370821,0.000005431208,0.0001965793,0.002875088],"genre_scores_gemma":[0.9659618,0.00002878186,0.02699783,0.004792794,0.0009864763,0.00001076816,0.0003450252,0.00003080107,0.0008457438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7205203,"threshold_uncertainty_score":0.6740047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01510550990543507,"score_gpt":0.377200725557779,"score_spread":0.3620952156523439,"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."}}