{"id":"W4407949536","doi":"10.1109/tia.2025.3546202","title":"Classification and Severity Estimation of Eccentricity Faults in Salient Pole Synchronous Machine Using Deep Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Industrial Automation and Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Centre for Studies in Religion and Society, University of Victoria; Alliance de recherche numérique du Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Salient; Eccentricity (behavior); Estimation; Computer science; Synchronous motor; Control theory (sociology); Artificial intelligence; Control engineering; Engineering; Electrical engineering; Psychology; Control (management)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000145208,0.0001307308,0.0001836918,0.0002964918,0.0001630317,0.00002070859,0.00008087265,0.0003108407,0.00002074262],"category_scores_gemma":[0.00000654859,0.0001521554,0.00003632599,0.0006980181,0.00004046331,0.000116452,0.000001186062,0.0005890332,0.000005242578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002542365,"about_ca_system_score_gemma":0.00004286103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002059283,"about_ca_topic_score_gemma":0.00006080911,"domain_scores_codex":[0.9990822,0.00006036566,0.0004181344,0.0001796937,0.000112489,0.0001471221],"domain_scores_gemma":[0.9995713,0.00007524311,0.00008005326,0.000180666,0.00004571239,0.00004699516],"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":[0.00001024791,0.00009476984,0.0006742145,0.00005701203,0.00002497476,1.479614e-7,0.0001038066,0.9201667,0.001748523,0.0003708406,0.000005219561,0.0767435],"study_design_scores_gemma":[0.0005236385,0.00001156624,0.005321329,0.00006976177,0.00003534218,0.000002442879,0.0002184594,0.9896144,0.003789373,0.00008320033,0.0002165053,0.0001139291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2251168,0.00005520829,0.7732531,0.00009228352,0.0001160897,0.0005472908,0.00002479161,0.0001458348,0.0006486845],"genre_scores_gemma":[0.9991735,0.00001876457,0.000463999,0.00001703897,0.00001695297,0.0002221154,0.00001042365,0.00001199356,0.00006525474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7740567,"threshold_uncertainty_score":0.6204717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01208405360949048,"score_gpt":0.2505269818602624,"score_spread":0.2384429282507719,"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."}}