{"id":"W2512801689","doi":"10.1109/tia.2016.2603962","title":"Design and Analysis of an Axial Flux Magnetically Geared Generator","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nova Scotia Department of Energy; U.S. Department of Energy","keywords":"Torque density; Torque; Topology (electrical circuits); Generator (circuit theory); Flux (metallurgy); Magnetic flux; Magnetic gear; Direct torque control; Electrical engineering; Mechanical engineering; Computer science; Physics; Engineering; Voltage; Magnetic field; Materials science; Power (physics); Quantum mechanics; Induction motor","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002619437,0.0003450694,0.0005315487,0.0003071794,0.0003074906,0.0006530595,0.0008524103,0.0006291356,0.003417365],"category_scores_gemma":[0.0004107244,0.0002484981,0.0003245169,0.0001740005,0.0003321128,0.0003539412,0.0002310747,0.0002758679,0.0008199864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004651977,"about_ca_system_score_gemma":0.0005027496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000620156,"about_ca_topic_score_gemma":0.0005060736,"domain_scores_codex":[0.99975,0.00004232403,0.000008504788,0.0000364405,0.0001473139,0.00001546641],"domain_scores_gemma":[0.9998117,0.00004263235,0.00005109451,0.00001554248,0.00006396919,0.0000150082],"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":[0.0004945075,0.0001734658,0.002292146,0.001491612,0.0001831985,0.001443181,0.0003506751,0.4952387,0.2757464,0.05106035,0.0049417,0.166584],"study_design_scores_gemma":[0.00006965054,0.0008015877,0.001265822,0.00004573256,0.00005400234,0.0004589349,0.00005154698,0.9529132,0.02276633,0.002788584,0.01875769,0.00002694713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05003074,0.0007103907,0.9188522,0.0003266697,0.0001336562,0.0003169004,0.0001268223,0.001250027,0.02825261],"genre_scores_gemma":[0.8657682,0.0003563759,0.1219522,0.00006797234,0.00004030898,0.0002373912,0.0001057859,0.00007211624,0.01139956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003417365,"threshold_uncertainty_score":0.01143223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01490157478735188,"score_gpt":0.226428099553954,"score_spread":0.2115265247666021,"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."}}