{"id":"W4382998806","doi":"10.1109/tpel.2023.3291464","title":"Gradient Boosting Decision Tree for Rotor Temperature Estimation in Permanent Magnet Synchronous Motors","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province","keywords":"Stator; Rotor (electric); Boosting (machine learning); Decision tree; Computer science; Magnet; Synchronous motor; Control theory (sociology); Control engineering; Artificial intelligence; Engineering; Mechanical engineering; Electrical engineering","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":[0.0002161158,0.0002431999,0.0002409548,0.0005801195,0.0001473526,0.00005180264,0.0001413474,0.0001555556,0.00004217792],"category_scores_gemma":[0.00001096161,0.0002513951,0.0001739413,0.001040055,0.00001175116,0.0001259344,6.201735e-7,0.0003898958,0.00004632161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005482596,"about_ca_system_score_gemma":0.00005483427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009153924,"about_ca_topic_score_gemma":0.0001544861,"domain_scores_codex":[0.9985271,0.00002457662,0.0003365503,0.0002966479,0.000230401,0.0005846964],"domain_scores_gemma":[0.9994246,0.0001865876,0.00003074533,0.0002358932,0.0000372102,0.00008494533],"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":[0.00006583268,0.0001043958,0.000007400023,0.00005073172,0.00008435885,0.00001020434,0.0003420828,0.854737,0.0206951,0.00005271548,0.001166141,0.122684],"study_design_scores_gemma":[0.0007371009,0.0004431637,0.000155051,0.00006092364,0.00006396149,0.000009218087,0.00002506393,0.9804046,0.01654704,0.0002810128,0.0009473277,0.0003254974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4056284,0.0004353224,0.5919457,0.00007267447,0.000416229,0.0007741751,0.00003038842,0.0005698141,0.0001272267],"genre_scores_gemma":[0.9970552,0.0003672725,0.001807453,0.0000308216,0.00002040687,0.0003288585,0.00002721464,0.00006561875,0.0002971846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5914267,"threshold_uncertainty_score":0.9999938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005978129815935584,"score_gpt":0.2221233895390168,"score_spread":0.2161452597230812,"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."}}