{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006758954,0.0005223548,0.000377456,0.0005356309,0.0003617073,0.0009338435,0.001197907,0.0007337882,0.003921728],"category_scores_gemma":[0.0006818865,0.000318512,0.0003937133,0.0005657956,0.0005340131,0.001436381,0.000534338,0.001026343,0.001894264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005754522,"about_ca_system_score_gemma":0.0004765436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003780331,"about_ca_topic_score_gemma":0.0005241208,"domain_scores_codex":[0.9995425,0.00005951323,0.00002381091,0.00009183851,0.0002437926,0.00003861278],"domain_scores_gemma":[0.9996679,0.0000597467,0.00004615982,0.00005522364,0.0001407601,0.00003035382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002164301,0.0003064371,0.00173726,0.00109011,0.00005391307,0.0003919637,0.0003627063,0.04586839,0.1677916,0.08950593,0.0114111,0.6812641],"study_design_scores_gemma":[0.00005569759,0.001254112,0.005367738,0.0003580487,0.00008741261,0.002019416,0.0002094161,0.2848632,0.09382635,0.03545807,0.5763534,0.0001471469],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04086298,0.02176542,0.8467038,0.002434612,0.001208288,0.0001349336,0.0001120372,0.001279669,0.08549818],"genre_scores_gemma":[0.6505136,0.01978508,0.267281,0.0005541538,0.001068529,0.0001941634,0.0002411333,0.0003254836,0.06003689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003921728,"threshold_uncertainty_score":0.01311946,"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."}}