{"id":"W4417013644","doi":"10.1016/j.mlwa.2025.100813","title":"Estimation of the remaining charge retention time of an electric vehicle battery","year":2025,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Electric vehicle; Electric-vehicle battery; Battery (electricity); Control theory (sociology); Charge (physics); Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002406736,0.0005190246,0.0003677858,0.0006855626,0.0001542288,0.0004537518,0.0005192638,0.0003291072,0.0007221372],"category_scores_gemma":[0.001405356,0.000144656,0.0003478099,0.0005066164,0.0001038477,0.0006851615,0.0002334666,0.0003271049,0.0004374507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003224396,"about_ca_system_score_gemma":0.0003734053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006432874,"about_ca_topic_score_gemma":0.005879349,"domain_scores_codex":[0.999889,0.000009075289,0.000007840269,0.0000317836,0.00004339773,0.00001884474],"domain_scores_gemma":[0.9996835,0.0001030582,0.0000564918,0.00002466412,0.0001177243,0.00001457337],"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":[0.0006097024,0.0001481816,0.05872485,0.0003728664,0.0001438097,0.000471867,0.0001480052,0.6210431,0.02623131,0.0008775814,0.002819512,0.2884092],"study_design_scores_gemma":[0.000004905611,0.0001058097,0.01564688,0.00002064416,0.00002898577,0.0001489666,0.00004578456,0.9710227,0.0114409,0.0005929454,0.0009196973,0.00002182588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8180475,0.001564086,0.1727738,0.0001691516,0.0000941064,0.00005258802,0.001474436,0.001612374,0.004211799],"genre_scores_gemma":[0.9917926,0.0001875594,0.006353249,0.00001619634,0.000009762536,0.00001778968,0.0006430834,0.00002839396,0.0009512444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006432874,"threshold_uncertainty_score":0.01279086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005618311864821722,"score_gpt":0.2439590877649142,"score_spread":0.2383407759000924,"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."}}