{"id":"W4406054666","doi":"10.34218/ijmet_15_06_002","title":"AN ENHANCED TEAGER HUANG TRANSFORM TECHNIQUE FOR BEARING FAULT DETECTION","year":2024,"lang":"en","type":"article","venue":"INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET)","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Optech (Canada); Lakehead University","funders":"","keywords":"Bearing (navigation); Fault detection and isolation; Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition; Fault (geology); Seismology; Geology","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":[0.0004378135,0.0001979132,0.0002557362,0.0009674345,0.00002764007,0.00008508259,0.0003911075,0.0003233446,0.0000102702],"category_scores_gemma":[0.0001615463,0.0001882116,0.0001151295,0.0002516519,0.00002266524,0.0003121973,0.00002888883,0.0006399729,0.000001425424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001374189,"about_ca_system_score_gemma":0.0000164458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000286325,"about_ca_topic_score_gemma":0.000003071845,"domain_scores_codex":[0.9989234,0.000007277673,0.0004583888,0.0001851414,0.0002163375,0.0002094631],"domain_scores_gemma":[0.9994565,0.0001257832,0.00004549426,0.0001221591,0.0001714071,0.0000786854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001461486,0.00002750869,0.000002274109,0.00008030801,0.0001874943,0.00003368506,0.00002854509,0.003270827,0.8581223,0.005008647,0.00003999541,0.1331838],"study_design_scores_gemma":[0.0002324767,0.0002489035,0.000006123725,0.000270625,0.00003511089,0.0003591206,0.00001517797,0.1826906,0.8071686,0.004918449,0.003884837,0.0001699675],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06467955,0.0006350686,0.9320312,0.000411469,0.0008895106,0.0002260022,0.00001161025,0.001067852,0.00004775498],"genre_scores_gemma":[0.9668381,0.0003120964,0.03241585,0.00001358131,0.0002282565,0.0001229986,0.000003254948,0.00005835629,0.000007514181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9021586,"threshold_uncertainty_score":0.7675048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004602948778659756,"score_gpt":0.2694519473728808,"score_spread":0.264848998594221,"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."}}