{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009754176,0.0001292624,0.0002059727,0.0003820372,0.00009050086,0.00001406321,0.0001221788,0.000231872,0.0004875956],"category_scores_gemma":[0.000001532389,0.0001090099,0.00008328695,0.001177843,0.00006378505,0.00008542179,4.277537e-7,0.0001930166,0.00001492022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003688322,"about_ca_system_score_gemma":0.00002504735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006174279,"about_ca_topic_score_gemma":0.000006152507,"domain_scores_codex":[0.9991977,0.00004498994,0.0002466351,0.000217706,0.000131524,0.0001615042],"domain_scores_gemma":[0.9993097,0.0001171355,0.00002890777,0.0003419669,0.00005599035,0.0001463513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001986385,0.0002220966,0.00003682608,0.00001224132,0.001199775,0.000001302582,0.00005826615,0.1313627,0.4350813,0.0002257615,0.0001960378,0.4315838],"study_design_scores_gemma":[0.0008028589,0.0002540612,0.001162515,0.00001839232,0.003412694,0.000007420162,0.00002371951,0.59094,0.4008848,0.0002825462,0.001638811,0.0005721942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04326123,0.00002392923,0.9560534,0.00007377656,0.00001903666,0.000209559,0.0001203292,0.0001308249,0.0001079347],"genre_scores_gemma":[0.9824957,0.00004826393,0.01644067,0.00002288007,0.00003704316,0.0003264948,0.000006735877,0.00002256639,0.0005996548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9396127,"threshold_uncertainty_score":0.533883,"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."}}