{"id":"W4388888499","doi":"10.1016/j.est.2023.109799","title":"Probabilistic lithium-ion battery state-of-health prediction using convolutional neural networks and Gaussian process regression","year":2023,"lang":"en","type":"article","venue":"Journal of Energy Storage","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Probabilistic logic; Kriging; State of health; Computer science; Context (archaeology); Battery (electricity); State of charge; Convolutional neural network; Gaussian process; Artificial neural network; Artificial intelligence; Machine learning; Engineering; Gaussian; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000438013,0.000532445,0.0005486672,0.0004915705,0.0001733356,0.0004854616,0.0007128711,0.0005742342,0.0007855713],"category_scores_gemma":[0.001693437,0.0002907302,0.0005151778,0.0004660977,0.0002386081,0.0006504752,0.0004018092,0.0007336519,0.0003299633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006453872,"about_ca_system_score_gemma":0.0005384755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01572833,"about_ca_topic_score_gemma":0.02095817,"domain_scores_codex":[0.9998736,0.00001711159,0.00000744684,0.00004438249,0.00002960145,0.00002784657],"domain_scores_gemma":[0.9995167,0.000259957,0.00006264921,0.00003081678,0.0001125971,0.00001727598],"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":[0.0003273239,0.0001387266,0.0129781,0.00005995721,0.0001051914,0.0001129157,0.0000321655,0.894935,0.002749448,0.001720621,0.002131844,0.08470874],"study_design_scores_gemma":[0.000001240419,0.000004285696,0.0005978984,0.000001279854,0.00000377756,0.000005164648,0.000001061692,0.9987429,0.0002554254,0.0003489457,0.00003641795,0.000001691216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4751068,0.001532713,0.5156529,0.0007373421,0.0001754117,0.00004462995,0.001405756,0.001883238,0.00346121],"genre_scores_gemma":[0.9910722,0.0001538873,0.006742552,0.0000429446,0.0000318529,0.00001230506,0.0004686494,0.00001601941,0.001459482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01572833,"threshold_uncertainty_score":0.03127354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02560175877572898,"score_gpt":0.2879966974858644,"score_spread":0.2623949387101354,"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."}}