{"id":"W3085507176","doi":"10.1109/itherm45881.2020.9190444","title":"A Thermal Management Design Methodology for Advanced Power Electronics Utilizing Genetic Optimization and Additive Manufacturing Techniques","year":2020,"lang":"en","type":"article","venue":"","topic":"Heat Transfer and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electronics; Power electronics; Computer science; Thermal management of electronic devices and systems; Power optimization; Power (physics); Manufacturing engineering; Engineering; Electrical engineering; Mechanical engineering; Power consumption","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":[0.0003355511,0.000522422,0.0002859175,0.0004074929,0.0002698046,0.0003800729,0.0005610577,0.0003596271,0.00116467],"category_scores_gemma":[0.0003543406,0.0002542803,0.0005185953,0.0003584915,0.0003866358,0.000251925,0.0003271439,0.0005312931,0.0002203039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004305285,"about_ca_system_score_gemma":0.0007207436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009131969,"about_ca_topic_score_gemma":0.001640415,"domain_scores_codex":[0.9998316,0.00003586488,0.000005437365,0.00002193817,0.0000900581,0.00001498774],"domain_scores_gemma":[0.9999162,0.00003110282,0.00001519856,0.000009424582,0.0000249121,0.000003142385],"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":[0.00001331304,0.00005044425,0.0002684183,0.00009524884,0.00003083643,0.00005875049,0.00004562242,0.8832213,0.03420569,0.02177763,0.0004682865,0.05976456],"study_design_scores_gemma":[0.00001315059,0.0001061842,0.0001321733,0.00001138899,0.00001630733,0.00004508224,0.00001151758,0.9811538,0.008939385,0.004581153,0.004981809,0.000008016527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01218947,0.000119697,0.9833534,0.00004598698,0.00002337306,0.00006138485,0.00001492687,0.0001539995,0.004037841],"genre_scores_gemma":[0.218267,0.000224018,0.7778284,0.00004800395,0.00001549269,0.0003183167,0.00004992401,0.00007667659,0.003172134],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00116467,"threshold_uncertainty_score":0.003896236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03149002777447915,"score_gpt":0.2536783868732114,"score_spread":0.2221883590987322,"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."}}