{"id":"W4313563575","doi":"10.1109/vppc55846.2022.10003429","title":"An Adaptive and Fast Health Estimation of Lithiumion Batteries Under Random Missing Data","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Vehicle Power and Propulsion Conference (VPPC)","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Missing data; Estimation; Computer science; Statistics; Mathematics; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004346692,0.0001604144,0.0002738427,0.000154178,0.0003130978,0.00006157342,0.0003292552,0.00005280496,0.000198041],"category_scores_gemma":[0.00003303498,0.0001543575,0.00001442331,0.0002230909,0.0001646326,0.0005016665,0.0003512951,0.0003911656,0.000001501855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006128887,"about_ca_system_score_gemma":0.000060199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000541914,"about_ca_topic_score_gemma":0.00001302053,"domain_scores_codex":[0.9986027,0.0001567605,0.0002844291,0.0003720891,0.0003094176,0.000274555],"domain_scores_gemma":[0.9991852,0.0000938412,0.0000780449,0.0005043736,0.00004387346,0.00009469096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005766429,0.0001082532,0.0007315918,0.0002811403,0.00004862739,0.000009373108,0.002112346,0.02040985,0.07411096,0.000443915,0.0008548248,0.9003125],"study_design_scores_gemma":[0.001529655,0.001054825,0.002797162,0.0000950527,0.00001216942,0.00002811443,0.005445078,0.9733152,0.01127765,0.003339543,0.0007407334,0.000364861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8161592,0.001162529,0.1789257,0.002118232,0.0002732321,0.0006014396,0.000307345,0.0003015191,0.0001507924],"genre_scores_gemma":[0.9961288,0.0003012199,0.00329912,0.0000791916,0.00001031984,0.0000354594,0.00008813406,0.00002456221,0.0000331683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9529053,"threshold_uncertainty_score":0.6294515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04651423122428387,"score_gpt":0.3116291781652124,"score_spread":0.2651149469409285,"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."}}