{"id":"W4319459237","doi":"10.1109/tte.2023.3242698","title":"Innovations in Axial Flux Permanent Magnet Motor Thermal Management for High Power Density Applications","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Stator; Torque density; Mechanical engineering; Powertrain; Rotor (electric); Magnet; Automotive engineering; Power density; Torque; Power (physics); Engineering; Electrical engineering; Materials science; Physics","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"],"consensus_categories":[],"category_scores_codex":[0.0002096131,0.0002090866,0.0001879825,0.0009344798,0.0001984443,0.00003006056,0.0001447075,0.0001359286,0.00009628097],"category_scores_gemma":[0.000001220799,0.0002469257,0.0001125949,0.002413899,0.00002284027,0.0001655038,2.414682e-8,0.0002204619,0.0001126791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001693709,"about_ca_system_score_gemma":0.00002502731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002020185,"about_ca_topic_score_gemma":0.00007408104,"domain_scores_codex":[0.9985464,0.00002624438,0.000501461,0.0003414515,0.0002478312,0.0003366771],"domain_scores_gemma":[0.9994207,0.0000754868,0.00005862882,0.0002742926,0.0001120433,0.00005884112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001510579,0.0004198485,0.00007173182,0.0001908549,0.0002413582,0.000006395635,0.0006553883,0.1709991,0.706936,0.01073376,0.0006311937,0.1089634],"study_design_scores_gemma":[0.004408141,0.00047782,0.1361905,0.00006592782,0.0006219827,0.000003237338,0.0002859006,0.1034491,0.746797,0.00346201,0.002667827,0.001570538],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1921923,0.00001766651,0.805267,0.0001653474,0.0001368373,0.001315869,0.000114902,0.0005735087,0.0002166403],"genre_scores_gemma":[0.9938138,0.000137372,0.001494803,0.00004521876,0.00003587479,0.002688114,0.0007647372,0.00005039584,0.0009697318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8037722,"threshold_uncertainty_score":0.9999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008900149050355178,"score_gpt":0.2138377198475838,"score_spread":0.2049375707972286,"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."}}