{"id":"W2012346040","doi":"10.1016/j.ress.2012.12.011","title":"A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft","year":2012,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":157,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Residual; Particle filter; Degradation (telecommunications); Population; Battery (electricity); Bayesian probability; Engineering; Computer science; Reliability engineering; Algorithm; Kalman filter; Artificial intelligence; Electronic 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":[0.002318555,0.000885192,0.001837049,0.0008688631,0.0004872088,0.001618051,0.002292479,0.001772235,0.00236004],"category_scores_gemma":[0.004991137,0.001097941,0.0009798724,0.0007019897,0.0008467339,0.001554882,0.001037898,0.001398218,0.0005634696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258172,"about_ca_system_score_gemma":0.001872742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02621643,"about_ca_topic_score_gemma":0.02472204,"domain_scores_codex":[0.9992848,0.0002341299,0.00003198917,0.0001660074,0.0001956764,0.0000873727],"domain_scores_gemma":[0.9982982,0.000988664,0.0001491002,0.0000921911,0.0003957381,0.0000760931],"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":[0.00004266152,0.00002700327,0.0003303807,0.00002850167,0.0000247778,0.00003340528,0.00002076626,0.9717847,0.0006158889,0.004904301,0.0005394425,0.02164821],"study_design_scores_gemma":[0.000002218865,0.000004971516,0.00007490136,0.000002524555,0.000003663806,0.000003896011,0.000001636783,0.9977649,0.00008646842,0.001951507,0.000100455,0.000002838984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009806806,0.0001802632,0.9885736,0.00008852967,0.00001318393,0.00002514236,0.0001026401,0.0002381952,0.0009717662],"genre_scores_gemma":[0.7872933,0.0005650216,0.2023845,0.0001309697,0.0001113881,0.000183742,0.0007781992,0.0001607449,0.008392277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02621643,"threshold_uncertainty_score":0.05212772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01854496582864961,"score_gpt":0.2748202455477143,"score_spread":0.2562752797190647,"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."}}