{"id":"W2017299800","doi":"10.1007/s10439-008-9601-1","title":"Analysis of Vibroarthrographic Signals with Features Related to Signal Variability and Radial-Basis Functions","year":2008,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":95,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Pattern recognition (psychology); Feature selection; Computer science; Articular cartilage; Artificial intelligence; Radial basis function; Artificial neural network; Data mining; Statistics; Mathematics; Medicine; Pathology","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":[0.0004941461,0.0003302075,0.0003260871,0.0008891358,0.0001455242,0.0003358155,0.0001928204,0.0002843097,0.0008676842],"category_scores_gemma":[0.002117992,0.0000839568,0.0003372065,0.0006994375,0.0001502873,0.0002923063,0.0001696652,0.0002644246,0.0002255017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008772984,"about_ca_system_score_gemma":0.0001597493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004529892,"about_ca_topic_score_gemma":0.0005163879,"domain_scores_codex":[0.99985,0.0000441105,0.000009757094,0.00003002924,0.00004989064,0.00001602886],"domain_scores_gemma":[0.9990068,0.0006101612,0.0001024394,0.00006436909,0.0001789021,0.00003733919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001861097,0.0002686448,0.01549953,0.0002323721,0.0002050643,0.0005636089,0.0001367256,0.0199488,0.4272889,0.001718486,0.0006427083,0.531634],"study_design_scores_gemma":[0.00005887981,0.0007612677,0.1997843,0.00003595326,0.0003896986,0.002456439,0.0001408222,0.7166374,0.07576542,0.001943128,0.001948995,0.00007779484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7022069,0.0007336651,0.2951123,0.00009570626,0.00003310704,0.00004108866,0.0001581408,0.0002935794,0.001325512],"genre_scores_gemma":[0.9473314,0.0002951748,0.0512578,0.00001669067,0.00004023367,0.00002416894,0.0002456525,0.00006139976,0.000727577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008891358,"threshold_uncertainty_score":0.002902746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351616229999032,"score_gpt":0.259378896080768,"score_spread":0.2458627337807777,"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."}}