{"id":"W4298012349","doi":"10.1016/j.ijheatmasstransfer.2022.123486","title":"A CNN-ABC model for estimation and optimization of heat generation rate and voltage distributions of lithium-ion batteries for electric vehicles","year":2022,"lang":"en","type":"article","venue":"International Journal of Heat and Mass Transfer","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":121,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Support vector machine; Voltage; Battery (electricity); Artificial neural network; Artificial intelligence; Power (physics); 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":[0.0002868899,0.0005927631,0.0006002769,0.0003048852,0.0002329614,0.0005681647,0.00108265,0.001045406,0.002450585],"category_scores_gemma":[0.0008084436,0.0003411963,0.000470396,0.0003923302,0.000236737,0.0004310244,0.000329288,0.0007473753,0.0004157996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008685775,"about_ca_system_score_gemma":0.0008508887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05636083,"about_ca_topic_score_gemma":0.04180278,"domain_scores_codex":[0.9999311,0.00001066837,0.000004033547,0.00002299501,0.0000170887,0.00001419178],"domain_scores_gemma":[0.9997978,0.00007489124,0.00001556756,0.0000103061,0.00009224007,0.000009202782],"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":[0.00004694682,0.00002840244,0.0007587585,0.00003607001,0.00002390357,0.00002977915,0.000008743124,0.9620929,0.001049919,0.001104894,0.0008294763,0.03399033],"study_design_scores_gemma":[8.649235e-7,0.000002633548,0.00004992525,0.000001173497,0.000001660393,0.000001742493,5.610122e-7,0.9997244,0.00007986186,0.00007425792,0.00006223266,5.928872e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1560635,0.003322578,0.8209897,0.0007881687,0.0003503558,0.000104411,0.0006983327,0.001442163,0.01624083],"genre_scores_gemma":[0.9537425,0.0005498666,0.03799763,0.0001505766,0.00004396838,0.0001122453,0.0005369454,0.0000584673,0.006807868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05636083,"threshold_uncertainty_score":0.1120656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01881896591827669,"score_gpt":0.2682329621556288,"score_spread":0.2494139962373521,"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."}}