{"id":"W3019587110","doi":"10.1038/s41598-020-64174-2","title":"A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Argonne National Laboratory; Ministry of Science and Technology of the People's Republic of China; Advanced Research Projects Agency - Energy; National Natural Science Foundation of China; Canada Excellence Research Chairs, Government of Canada; U.S. Department of Energy","keywords":"Homogeneity (statistics); Computer science; Battery (electricity); Voltage; Lithium-ion battery; Electrical impedance; Current source; Electrochemistry; Materials science; Electrical engineering; Electrode; Chemistry; Physics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001565477,0.001347022,0.0009192327,0.001295625,0.0003937152,0.0007984488,0.001206098,0.001281162,0.001300257],"category_scores_gemma":[0.002718057,0.0006301858,0.0004600464,0.001076193,0.0006811559,0.00128208,0.001409587,0.002082238,0.001038334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004810821,"about_ca_system_score_gemma":0.0006479698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004788517,"about_ca_topic_score_gemma":0.001262486,"domain_scores_codex":[0.9985266,0.0001645661,0.00009928887,0.0004354848,0.0006974165,0.00007659983],"domain_scores_gemma":[0.9991174,0.0002722412,0.0001304618,0.000137695,0.000304013,0.00003826263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003322187,0.00004302035,0.0005099916,0.0002234459,0.00001887895,0.0001159435,0.00009329849,0.0004125188,0.9715778,0.00110123,0.0007509987,0.02511954],"study_design_scores_gemma":[0.0000105553,0.0001546726,0.0008696727,0.00001931475,0.00002878034,0.0003683573,0.00005617217,0.006470763,0.9844301,0.000708253,0.00683136,0.00005211452],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08339677,0.006441755,0.9006281,0.0006518238,0.0006785915,0.0003922928,0.001033686,0.002781989,0.003994935],"genre_scores_gemma":[0.4662808,0.005763758,0.5213488,0.0008088267,0.0002359706,0.001093528,0.0009004082,0.0001947743,0.003373103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001565477,"threshold_uncertainty_score":0.008279085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02021290864373154,"score_gpt":0.2372096580473996,"score_spread":0.216996749403668,"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."}}