{"id":"W2956063092","doi":"10.1016/j.egyr.2019.06.016","title":"Electric vehicle battery thermal management system with thermoelectric cooling","year":2019,"lang":"en","type":"article","venue":"Energy Reports","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":365,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Thermoelectric cooling; Computer cooling; Battery (electricity); Water cooling; Coolant; Materials science; Nuclear engineering; Air cooling; Thermoelectric effect; Condenser (optics); TEC; Automotive engineering; Mechanical engineering; Thermal management of electronic devices and systems; Power (physics); Engineering; Thermodynamics","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":[0.0001158129,0.0002014234,0.0002124304,0.0002642628,0.00004993806,0.00003501587,0.000190484,0.0000864801,0.00005204898],"category_scores_gemma":[0.000003379608,0.0001669142,0.00003925468,0.0006883998,0.0000154647,0.0001315002,0.0000695081,0.000210656,0.00004425018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002392706,"about_ca_system_score_gemma":0.0000137851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001190598,"about_ca_topic_score_gemma":0.000002365078,"domain_scores_codex":[0.9985113,0.00001817967,0.0002593285,0.0003332092,0.0003353071,0.0005427081],"domain_scores_gemma":[0.999145,0.00003030862,0.00006354283,0.0006777207,0.00003262148,0.00005085081],"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":[0.000103643,0.00005829808,0.01081059,0.0005238419,0.000508037,0.00720443,0.00004006807,0.6505272,0.1586511,0.002328364,0.0006898784,0.1685546],"study_design_scores_gemma":[0.001647138,0.0008691911,0.02977017,0.0005816116,0.00009896355,0.003881104,0.0004501275,0.4363402,0.4965229,0.0002764972,0.02704014,0.002521958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9393968,0.0005536781,0.01859003,0.00001542535,0.0002138765,0.0002161909,3.134271e-7,0.001670096,0.0393436],"genre_scores_gemma":[0.9980492,0.00004911371,0.0003495387,0.00002494116,0.00004230232,0.00008990921,0.000004462186,0.00007937595,0.001311173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3378719,"threshold_uncertainty_score":0.6806563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004034776204503199,"score_gpt":0.1864127140979547,"score_spread":0.1823779378934514,"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."}}