{"id":"W4388742679","doi":"10.1145/3626562.3626829","title":"Federated Computing in Electric Vehicles to Predict Coolant Temperature","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie","keywords":"Computer science; Overheating (electricity); Cluster analysis; Coolant; Architecture; Greenhouse gas; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009276549,0.0007233123,0.00052771,0.0005686892,0.0004102978,0.0009739571,0.0009118614,0.0005717046,0.0006080916],"category_scores_gemma":[0.002486159,0.0001987114,0.000396883,0.0008766339,0.0003544434,0.001726523,0.0006269377,0.0007797895,0.0002606793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008942224,"about_ca_system_score_gemma":0.0008052886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01316378,"about_ca_topic_score_gemma":0.009619673,"domain_scores_codex":[0.9995371,0.0001442873,0.00002162791,0.0001427223,0.0000869548,0.00006723859],"domain_scores_gemma":[0.9993176,0.0002574644,0.00006002585,0.0001744918,0.0001485025,0.00004178683],"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":[0.000199664,0.000139417,0.008264063,0.00004737603,0.00005883662,0.00008391369,0.00005276617,0.9230579,0.001548429,0.003443092,0.002547371,0.0605572],"study_design_scores_gemma":[0.000003755156,0.0000159434,0.0009241429,0.00000607364,0.000005604683,0.00001774041,0.00002641303,0.9933138,0.00103384,0.003949185,0.0006986299,0.000004842056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5902681,0.002264045,0.391444,0.001925279,0.0003293004,0.00009299441,0.001386599,0.005029204,0.007260459],"genre_scores_gemma":[0.983382,0.0002038538,0.01494422,0.00008210604,0.00001918576,0.00002189476,0.0006134683,0.00003689047,0.0006962194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01316378,"threshold_uncertainty_score":0.02617431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437215618756674,"score_gpt":0.2640612514217177,"score_spread":0.2496890952341509,"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."}}