{"id":"W2773345598","doi":"10.1109/pcicon.2017.8188747","title":"Lithium-ion batteries for industrial applications","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"MPB Technologies & Communications (Canada)","funders":"","keywords":"Lithium (medication); Energy storage; Automotive industry; Computer science; Lead–acid battery; Energy density; Process engineering; Automotive engineering; Power (physics); Engineering; Battery (electricity); Engineering 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004756606,0.00006422662,0.00007127422,0.00004168419,0.0002143427,0.00008487538,0.0003896993,0.00009430567,0.00005580286],"category_scores_gemma":[0.00009478911,0.00005944273,0.00002185401,0.00002673549,0.00008151057,0.0001821835,0.0000898479,0.0001189145,0.00005116702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003152266,"about_ca_system_score_gemma":0.000006001723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003072816,"about_ca_topic_score_gemma":0.000007405385,"domain_scores_codex":[0.9995764,0.000001623141,0.00008083561,0.0001016349,0.00006030052,0.0001792224],"domain_scores_gemma":[0.9993472,0.00004442217,0.0000155832,0.0005436749,0.00002515609,0.00002397985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000246169,0.00002619729,0.002537352,0.00009353984,0.00005067445,0.000001414195,0.0000337346,0.001031341,0.04892663,0.01603447,0.06465098,0.8665891],"study_design_scores_gemma":[0.0004844448,0.0000380087,0.0007405482,0.000008718055,0.000003698997,0.000001575632,0.00007261471,0.002816129,0.1808429,0.007110087,0.8076921,0.0001892558],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02767138,0.00004503827,0.9295762,0.002798806,0.0004023375,0.001295011,0.00005377744,0.001840768,0.03631672],"genre_scores_gemma":[0.981816,0.00004013126,0.01345475,0.00002862225,0.0003324506,0.001269892,0.00001398162,0.00003456132,0.003009633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9541446,"threshold_uncertainty_score":0.2424004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06515537975481177,"score_gpt":0.3232666797118795,"score_spread":0.2581112999570677,"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."}}