{"id":"W1806785997","doi":"","title":"Effects of multi-talker background noise on the intensity of spoken sentences in parkinson's disease","year":2005,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Intensity (physics); Parkinson's disease; Audiology; Noise (video); Background noise; Medicine; Speech recognition; Disease; Acoustics; Computer science; Physics; Artificial intelligence; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001082173,0.0001044456,0.0002125239,0.0001440164,0.00002864094,0.000004833762,0.0001012225,0.00005685682,0.00004657308],"category_scores_gemma":[0.0004099068,0.0000767668,0.0000610809,0.0001696078,0.0001475462,0.00002919891,0.00001157157,0.0001535896,0.00001792288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001176942,"about_ca_system_score_gemma":0.0003680098,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01448747,"about_ca_topic_score_gemma":0.03450944,"domain_scores_codex":[0.9992897,0.00002393531,0.0001696273,0.0001320663,0.0001564792,0.0002282112],"domain_scores_gemma":[0.999172,0.0001374057,0.00005463017,0.0002264204,0.00009402524,0.000315478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001298777,0.002048194,0.8632102,0.002462548,0.0003493994,0.001924684,0.00390871,0.004017102,0.04998724,0.0007409781,0.04360553,0.02644663],"study_design_scores_gemma":[0.0009455906,0.00009066675,0.986645,0.0002776091,0.0001204355,0.000003023666,0.001213476,0.006552,0.001011168,0.00002209541,0.003013334,0.0001055363],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959774,0.0003249102,0.00005212961,0.002845274,0.00009140546,0.0003502678,0.00003345585,0.000005726937,0.0003194108],"genre_scores_gemma":[0.9966696,0.0001207936,0.0002027953,0.002640459,0.00005719913,0.000005490792,0.000007712641,0.00001174741,0.0002841917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1234349,"threshold_uncertainty_score":0.9920751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01796075434628023,"score_gpt":0.2476992249012991,"score_spread":0.2297384705550188,"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."}}