{"id":"W2992121034","doi":"","title":"Generative Design Optimization of Thermal Management Systems for High Output Power Electronics","year":2019,"lang":"","type":"dissertation","venue":"TSpace","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electronics; Power electronics; Thermal management of electronic devices and systems; Generative grammar; Power (physics); Computer science; Electrical engineering; Engineering; Mechanical engineering; Artificial intelligence; 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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005306415,0.0005539262,0.0007913038,0.0002623035,0.0005345929,0.0004240345,0.0003088157,0.0002899512,0.00111844],"category_scores_gemma":[0.00001965141,0.0005334761,0.0002402498,0.00008764962,0.00009463186,0.0002007605,0.00003966379,0.0002365226,0.00004344077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002299603,"about_ca_system_score_gemma":0.0002546465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003208317,"about_ca_topic_score_gemma":0.0001402661,"domain_scores_codex":[0.9973947,0.0002691095,0.0007502154,0.0005871459,0.000467906,0.0005308606],"domain_scores_gemma":[0.9973608,0.0001339299,0.0009972081,0.0004068928,0.001030429,0.00007073546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007735444,0.000387661,0.00000811425,0.001801264,0.001645195,0.000003104385,0.04541003,0.4547979,0.0007758426,0.4900844,0.003948045,0.0003649643],"study_design_scores_gemma":[0.01059428,0.01064557,0.0004937567,0.003074156,0.00513505,0.000002730995,0.2435805,0.5848871,0.02060697,0.002132787,0.1132447,0.005602319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3379127,0.02191736,0.4407447,0.00058205,0.02304597,0.03034085,0.000823099,0.0001987623,0.1444345],"genre_scores_gemma":[0.7249022,0.001745673,0.0009735285,0.00003515074,0.000309483,0.0003240816,0.0008335763,0.0001190263,0.2707573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4879516,"threshold_uncertainty_score":0.9997947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03149924175342258,"score_gpt":0.2816022968119863,"score_spread":0.2501030550585637,"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."}}