{"id":"W1885653237","doi":"10.1109/tfsa.1998.721414","title":"Comparative analysis of the performance of the time-frequency distributions with knee joint vibroarthrographic signals","year":2002,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Time–frequency analysis; Matching pursuit; SIGNAL (programming language); Computer science; Auscultation; Autoregressive model; Speech recognition; Pattern recognition (psychology); Decomposition; Artificial intelligence; Algorithm; Noise (video); Joint (building); Identification (biology); Signal processing; Mathematics; Engineering; Statistics; Computer vision; Filter (signal processing); Radar; Telecommunications; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.00007166058,0.0001238346,0.0003027379,0.0001148236,0.0000631798,0.000007734814,0.0002353764,0.00003531056,0.0006072028],"category_scores_gemma":[0.000009772149,0.00006231166,0.0001950875,0.001819266,0.0001763391,0.00007317543,0.00003673178,0.000125464,0.00000411894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002043549,"about_ca_system_score_gemma":0.000005057246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005442473,"about_ca_topic_score_gemma":0.0001006053,"domain_scores_codex":[0.9992702,0.00004042063,0.0002654883,0.00009499316,0.0002006708,0.000128254],"domain_scores_gemma":[0.9992825,0.0000540587,0.00008912096,0.0004735301,0.00007813354,0.00002262192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008223908,0.0006049737,0.6944782,0.0001920584,0.004700004,5.788222e-7,0.001786965,0.1798275,0.1015902,0.004656348,0.0111242,0.001030764],"study_design_scores_gemma":[0.0001410562,0.0001241151,0.290222,0.00009543864,0.0006371182,0.000001264799,0.00002651493,0.3532402,0.3551494,0.00005715077,0.0001328845,0.0001728691],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896922,0.0001449788,0.0009232375,0.0000894604,0.00001382919,0.0002779012,0.00008049372,0.0001199091,0.008658014],"genre_scores_gemma":[0.9991765,0.00003617428,0.0006545292,0.00001277589,0.000004307447,0.00004167165,0.000005601964,0.000006499155,0.00006194991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4042562,"threshold_uncertainty_score":0.6648445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0121312757456182,"score_gpt":0.2339640195478205,"score_spread":0.2218327438022022,"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."}}