{"id":"W4398769495","doi":"10.1016/j.knosys.2024.111999","title":"Deep temporal–spectral domain adaptation for bearing fault diagnosis","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Normalization (sociology); Deep learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005844416,0.0003685754,0.00039443,0.0004205637,0.0001090634,0.0003113888,0.0002651592,0.0002118245,0.00002681287],"category_scores_gemma":[0.00008076518,0.0003677846,0.0002362173,0.0004652492,0.00003126559,0.0002162554,0.00002092035,0.000252726,0.0001491174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004234349,"about_ca_system_score_gemma":0.00006819858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001514116,"about_ca_topic_score_gemma":0.0004315074,"domain_scores_codex":[0.9982303,0.00008252482,0.0005521529,0.0004455258,0.0002119521,0.0004775241],"domain_scores_gemma":[0.9987117,0.0006273368,0.00004274261,0.0003757497,0.0001010784,0.0001414096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001066507,0.0009270167,0.03175577,0.03512723,0.001228708,0.0001975577,0.01071772,0.5761387,0.01349906,0.04136636,0.201285,0.08765019],"study_design_scores_gemma":[0.0003548122,0.00009843438,0.0002127595,0.0008868033,0.00004747154,0.000005054415,0.0001389329,0.8763482,0.008361991,0.000391809,0.1127108,0.0004429657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04734609,0.02397398,0.907885,0.0001614659,0.003904996,0.002630215,0.0001119334,0.006949391,0.007036975],"genre_scores_gemma":[0.9872469,0.00004712216,0.007026543,0.0000127838,0.0007640568,0.004446291,0.0001285023,0.0001746176,0.000153238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9399008,"threshold_uncertainty_score":0.9998774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01836072008603921,"score_gpt":0.2827193726097089,"score_spread":0.2643586525236697,"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."}}