{"id":"W4400945537","doi":"10.1109/itec60657.2024.10599019","title":"Estimation of equivalent thermal conductivity of impregnated slots in electric machines using Artificial Neural Network Surrogate Model","year":2024,"lang":"en","type":"article","venue":"","topic":"Induction Heating and Inverter Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Mitacs","keywords":"Artificial neural network; Thermal conductivity; Surrogate model; Computer science; Materials science; Artificial intelligence; Biological system; Machine learning; Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001411917,0.00009286527,0.0001651882,0.0001952895,0.0000167665,0.000007390836,0.00005493965,0.00009263954,0.00001517897],"category_scores_gemma":[0.00001225136,0.00008617025,0.00003724377,0.0004744215,0.00002666956,0.0001392198,0.00001515536,0.0001715694,0.000001066683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004603017,"about_ca_system_score_gemma":0.00001987526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001468657,"about_ca_topic_score_gemma":0.00001628622,"domain_scores_codex":[0.999329,0.00002171678,0.0002889614,0.0001084756,0.00007470208,0.000177194],"domain_scores_gemma":[0.9998165,0.00002684041,0.00002507456,0.00009745859,0.00002165138,0.00001246355],"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":[0.000004301306,0.000009878346,0.00007254004,0.0000711215,0.00001049597,7.423193e-7,0.00005640033,0.7074286,0.2704652,0.00179001,0.000007804692,0.02008295],"study_design_scores_gemma":[0.00005071421,0.00002455329,0.00009600501,0.0000490252,0.000009318525,0.000004958567,0.000006741224,0.8718417,0.1261307,0.001722214,1.878268e-7,0.00006391275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723229,0.0002445594,0.02650106,0.0000200946,0.0003241713,0.00008879403,0.000002506606,0.0002815086,0.0002144383],"genre_scores_gemma":[0.9983702,0.000004744265,0.001558722,0.000002811247,0.00003286883,0.000003466417,0.000003128804,0.00001661779,0.00000744175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1644132,"threshold_uncertainty_score":0.3513921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03733518821840391,"score_gpt":0.2718063329459318,"score_spread":0.2344711447275278,"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."}}