{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005453926,0.0008042817,0.001021316,0.001267335,0.0003242772,0.0008625499,0.0003876195,0.0006187697,0.001201129],"category_scores_gemma":[0.001225862,0.000263817,0.0006385333,0.0009381312,0.0002576996,0.001235195,0.0009011612,0.0006504197,0.0007570786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001937504,"about_ca_system_score_gemma":0.0003765087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001137976,"about_ca_topic_score_gemma":0.001404891,"domain_scores_codex":[0.9996514,0.00003768234,0.00002260728,0.00009040885,0.0001306796,0.00006711814],"domain_scores_gemma":[0.9996322,0.0001002446,0.00005675885,0.00006876545,0.000119104,0.00002294584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004975759,0.0001888468,0.004778364,0.00009260906,0.000127434,0.000143675,0.00007316554,0.07611293,0.07144722,0.001866898,0.004184164,0.8404871],"study_design_scores_gemma":[0.00001331449,0.0001214897,0.005954899,0.00001176798,0.00005445142,0.0001127929,0.00002750801,0.9777097,0.0120058,0.002858031,0.00111205,0.00001822442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09662802,0.0006632279,0.8985797,0.0002194373,0.0001068795,0.00004447078,0.000288711,0.001395503,0.002074053],"genre_scores_gemma":[0.8962904,0.0002677212,0.1003132,0.00007208326,0.00009046821,0.00003345881,0.0008502629,0.00007256884,0.00200971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001267335,"threshold_uncertainty_score":0.004018128,"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."}}