{"id":"W4221079999","doi":"10.1016/j.measurement.2022.111046","title":"Gated recurrent unit least-squares generative adversarial network for battery cycle life prediction","year":2022,"lang":"en","type":"article","venue":"Measurement","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Prognostics; Discriminator; Computer science; Perceptron; Battery (electricity); Set (abstract data type); Generator (circuit theory); Artificial intelligence; Data mining; Machine learning; Artificial neural network; Reliability engineering; Engineering; Detector","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":[0.0005591777,0.0001756091,0.0001806651,0.0001110154,0.0003492296,0.00002590435,0.0002641397,0.00005241077,0.0002239662],"category_scores_gemma":[0.0001355564,0.0001913955,0.00006463933,0.0003077287,0.00003245523,0.00009292761,0.000150587,0.0003677917,0.00001215043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000737023,"about_ca_system_score_gemma":0.00005797776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005648013,"about_ca_topic_score_gemma":0.00001862531,"domain_scores_codex":[0.9981546,0.00009152035,0.0002669398,0.0002660244,0.0007429956,0.0004778514],"domain_scores_gemma":[0.999416,0.00004030845,0.00004002901,0.0002878128,0.000138841,0.00007699952],"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":[0.0001365831,0.00005141883,0.0004692379,0.00004203676,0.0001183317,0.000002287475,0.00009249328,0.9372796,0.005339842,0.00005916982,0.04062062,0.01578839],"study_design_scores_gemma":[0.003342063,0.001321461,0.00319981,0.00007631695,0.00007243161,0.000005933435,0.001008427,0.5894178,0.01540309,0.001566088,0.3838304,0.0007562107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1464144,0.003601292,0.8290392,0.00217642,0.008624442,0.004433712,0.0007404819,0.003651944,0.00131814],"genre_scores_gemma":[0.9958352,0.00003904893,0.0023019,0.00009402775,0.0004695536,0.001050804,0.0001090228,0.00005543939,0.00004502786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8494208,"threshold_uncertainty_score":0.780488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0635894485596158,"score_gpt":0.2640243288650511,"score_spread":0.2004348803054353,"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."}}