{"id":"W2104006671","doi":"10.1109/icassp.2011.5947274","title":"Compact support kernels based time-frequency distributions: Performance evaluation","year":2011,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Interference (communication); Time–frequency analysis; Measure (data warehouse); Quadratic equation; Instantaneous phase; Noise (video); Component (thermodynamics); Polynomial; Energy (signal processing); Computer science; Algorithm; Nonlinear system; Separable space; Resolution (logic); Frequency distribution; Mathematics; Mathematical optimization; Artificial intelligence; Statistics; Mathematical analysis; Telecommunications; Data mining; Physics","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":[0.001790937,0.0007128817,0.000799427,0.001009584,0.0002138141,0.0009487384,0.0007063326,0.0007321017,0.0016752],"category_scores_gemma":[0.008636336,0.0001350845,0.0002847587,0.0007930374,0.0006179726,0.001234196,0.0009132696,0.0005821962,0.0005153406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006840814,"about_ca_system_score_gemma":0.000355194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001544871,"about_ca_topic_score_gemma":0.0006027639,"domain_scores_codex":[0.9988134,0.00028095,0.00006876326,0.000118444,0.0006041322,0.000114218],"domain_scores_gemma":[0.9951655,0.00310884,0.0003864014,0.0005261453,0.0006260327,0.0001871816],"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":[0.005398441,0.0003023335,0.003597024,0.0006085639,0.0001761826,0.000261575,0.0002774305,0.446573,0.05127247,0.01179597,0.001447907,0.4782891],"study_design_scores_gemma":[0.00004850435,0.0004747261,0.001135262,0.00001229336,0.00002202416,0.0003382582,0.00004438153,0.9634265,0.03252674,0.00110779,0.0008400329,0.00002357904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2017472,0.0009819391,0.7926815,0.0001526343,0.00004745746,0.00005751177,0.00009700878,0.00182879,0.002405979],"genre_scores_gemma":[0.923739,0.0003231145,0.07478904,0.00002440783,0.00002288892,0.00002994254,0.0001765397,0.0001082606,0.0007867494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001790937,"threshold_uncertainty_score":0.009471536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03036051421836909,"score_gpt":0.2797269774523687,"score_spread":0.2493664632339996,"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."}}