{"id":"W4402043823","doi":"10.2139/ssrn.4941746","title":"Intelligent Rotor Imbalance Fault Classification Through Comprehensive Feature Fusion","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fault (geology); Fusion; Rotor (electric); Feature (linguistics); Artificial intelligence; Computer science; Pattern recognition (psychology); Engineering; Geology; Seismology; Electrical engineering","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003945593,0.0004703127,0.0004148842,0.0001220186,0.00009297216,0.0001649873,0.0004465097,0.0005762422,0.00001313304],"category_scores_gemma":[0.00005335481,0.0004136837,0.0002994698,0.0001718237,0.0000422375,0.00005191461,0.0002570057,0.0101698,0.0001011409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002274083,"about_ca_system_score_gemma":0.0006731762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001959621,"about_ca_topic_score_gemma":0.00005365247,"domain_scores_codex":[0.9972032,0.00004006057,0.0004322137,0.0004162397,0.0003427824,0.001565486],"domain_scores_gemma":[0.9990709,0.00008427053,0.0001014587,0.0004644167,0.00018022,0.00009875957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004723839,0.0001731779,0.0002551207,0.002365709,0.00177664,0.00005026587,0.001097885,0.8494001,0.007243082,0.0878136,0.01769243,0.03208475],"study_design_scores_gemma":[0.0004272668,0.0002381348,0.001451934,0.001711541,0.0003407928,0.0004173376,0.0008231923,0.2410398,0.001670768,0.6151376,0.1351957,0.001545964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4018597,0.2996191,0.2552515,0.005950177,0.02823734,0.002480389,0.000199993,0.002285096,0.004116716],"genre_scores_gemma":[0.8940565,0.1032066,0.0008356422,0.00003686538,0.001084966,0.00005956195,0.00007704202,0.0001305802,0.0005122141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6083604,"threshold_uncertainty_score":0.9998315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116729522803184,"score_gpt":0.2508613436032988,"score_spread":0.2391883913229804,"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."}}