{"id":"W2061107170","doi":"10.1007/s11634-014-0168-4","title":"Feature selection for fault level diagnosis of planetary gearboxes","year":2014,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Feature selection; Pattern recognition (psychology); Feature (linguistics); Fault (geology); Computer science; Artificial intelligence; Kernel (algebra); Minimum redundancy feature selection; Similarity (geometry); Selection (genetic algorithm); Feature vector; Feature extraction; Ranking (information retrieval); Algorithm; Data mining; Mathematics","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":[0.0002030135,0.00007515824,0.0001975079,0.0002255238,0.00003027321,0.00001836212,0.0001508555,0.00005177122,0.00000527837],"category_scores_gemma":[0.00004844538,0.00007022228,0.00004149941,0.0005737011,0.0000196379,0.0002339068,0.00001904764,0.00005769128,6.937461e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001215206,"about_ca_system_score_gemma":0.000002583718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004384577,"about_ca_topic_score_gemma":0.003441273,"domain_scores_codex":[0.9994493,0.00001614681,0.0001533875,0.0002128725,0.0000805161,0.00008776726],"domain_scores_gemma":[0.999496,0.00009336637,0.00005626143,0.0003032996,0.00003089846,0.00002018188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001306036,0.00004857813,0.6240671,0.0001791093,0.0005548287,6.596313e-8,0.00004683783,0.1759266,0.002002685,0.001354235,0.0005465071,0.1952604],"study_design_scores_gemma":[0.00008143098,0.000008986393,0.1459514,0.000007206158,0.0003782716,1.336536e-7,0.00002677263,0.8435184,0.0001552588,0.0001983441,0.009602683,0.00007118942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2606307,0.002924923,0.7346135,0.000381171,0.00008795833,0.0002027349,0.0006793802,0.00006770562,0.0004119503],"genre_scores_gemma":[0.9874693,0.003319339,0.007210471,0.000005857026,0.0000256954,0.00002034137,0.00190905,0.000006651907,0.000033317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.727403,"threshold_uncertainty_score":0.2863582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02554607314779903,"score_gpt":0.2725611842871389,"score_spread":0.2470151111393399,"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."}}