{"id":"W1993322157","doi":"10.4271/2011-01-1370","title":"Optimizing Electric Vehicle Battery Life through Battery Thermal Management","year":2011,"lang":"en","type":"article","venue":"SAE International Journal of Engines","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Battery (electricity); Automotive engineering; Electric vehicle; Automotive battery; Electric-vehicle battery; Thermal management of electronic devices and systems; Computer science; Engineering; Environmental science; Mechanical engineering; Power (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":[],"consensus_categories":[],"category_scores_codex":[0.0001424946,0.0001692381,0.0001869064,0.0004242235,0.0000274226,0.00004148982,0.0008602214,0.00006781968,0.0002883059],"category_scores_gemma":[0.00005644834,0.0001519656,0.0001101022,0.0002055816,0.00003459193,0.0006104296,0.0001278497,0.000384273,0.00004672601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001315729,"about_ca_system_score_gemma":0.00001522147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001995097,"about_ca_topic_score_gemma":6.323507e-7,"domain_scores_codex":[0.9986787,0.00001707599,0.000427435,0.0001182857,0.0004616299,0.000296862],"domain_scores_gemma":[0.9993972,0.00006539762,0.0001008878,0.0001714446,0.0002026357,0.00006250231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005285456,0.0005825452,0.01217259,0.0002978141,0.005478034,0.003553102,0.002833976,0.4729935,0.2726922,0.003551512,0.0179476,0.2073686],"study_design_scores_gemma":[0.01029142,0.001385742,0.2835301,0.001721765,0.0003621552,0.001767132,0.003693137,0.0947483,0.5263606,0.0177868,0.05477759,0.00357525],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8872004,0.001691371,0.09504515,0.0006984621,0.00249933,0.0001455613,0.000006435029,0.0003325593,0.01238072],"genre_scores_gemma":[0.9800579,0.000743169,0.01854047,0.0001899175,0.0003469158,0.000008089467,0.000001651487,0.00004165523,0.0000702379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3782452,"threshold_uncertainty_score":0.6196978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02642013937188755,"score_gpt":0.2622200799758556,"score_spread":0.235799940603968,"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."}}