{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005387207,0.00050051,0.0003525885,0.0003242271,0.0003222755,0.0007868347,0.0005464145,0.0004499982,0.003027423],"category_scores_gemma":[0.00109859,0.0003068598,0.0005580314,0.0002808754,0.0008324776,0.0003995432,0.0006250553,0.0006102703,0.0002769038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008414516,"about_ca_system_score_gemma":0.0008265289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001312116,"about_ca_topic_score_gemma":0.001995428,"domain_scores_codex":[0.9997887,0.00007784304,0.000005124311,0.00002765446,0.00007462448,0.00002604607],"domain_scores_gemma":[0.9997774,0.0001283009,0.0000295148,0.00001975852,0.00003479302,0.00001024413],"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":[0.0000117464,0.00002296766,0.0002005428,0.00003429247,0.00001171307,0.00002161578,0.00005818533,0.9653769,0.002130212,0.0208431,0.0002435115,0.01104523],"study_design_scores_gemma":[0.000007113601,0.00002912024,0.00006840414,0.000005686578,0.000004656883,0.000006634805,0.00001795458,0.9919117,0.0007324437,0.006075056,0.001138974,0.000002340252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06364163,0.0002403589,0.9155087,0.0002079447,0.00003417865,0.00007775395,0.00003820501,0.0002071343,0.0200441],"genre_scores_gemma":[0.7941558,0.0003403885,0.1969754,0.00009600875,0.00002090916,0.0002229137,0.00008095657,0.0001417622,0.007965746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003027423,"threshold_uncertainty_score":0.01012766,"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."}}