{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007243916,0.0002474077,0.0003879398,0.0001771031,0.00006930016,0.00001870154,0.0001458826,0.0002508411,0.000007814923],"category_scores_gemma":[0.0004875502,0.0002460684,0.00005973152,0.0002206044,0.00005573891,0.0002976065,0.00005509858,0.0004350563,0.000002059905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000500457,"about_ca_system_score_gemma":0.00003766675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000387081,"about_ca_topic_score_gemma":6.507654e-7,"domain_scores_codex":[0.9984512,0.00003776926,0.0005481064,0.0002567478,0.0002719932,0.0004342395],"domain_scores_gemma":[0.9985285,0.0006476943,0.00006691187,0.0005373719,0.00007826511,0.0001412696],"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":[0.0002331235,0.000176108,0.03252295,0.01528293,0.0003013766,0.000001730394,0.0005319573,0.9014239,0.01232238,0.02337221,0.0004709303,0.01336041],"study_design_scores_gemma":[0.001305141,0.0006894074,0.1723209,0.00154598,0.00007626745,0.0000220107,0.001025222,0.8022593,0.01457783,0.0003975497,0.005014689,0.0007657503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1641748,0.0002552083,0.8330172,0.0001775331,0.0004385838,0.0007275784,0.0002290306,0.0008903888,0.00008968771],"genre_scores_gemma":[0.9386865,0.00005819722,0.0607729,0.00000596657,0.0001594401,0.0001944064,0.00005571195,0.0000589327,0.000007892363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7745118,"threshold_uncertainty_score":0.9999992,"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."}}