{"id":"W4366713939","doi":"10.1016/j.est.2023.107376","title":"Modeling of capacitance for carbon-based supercapacitors using Super Learner algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Energy Storage","topic":"Supercapacitor Materials and Fabrication","field":"Materials Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Shahrood University of Technology","keywords":"Capacitance; Supercapacitor; Materials science; Carbon fibers; Microporous material; Electrode; Capacitor; Analytical Chemistry (journal); Voltage; Chemistry; Composite material; Electrical engineering; Engineering; Chromatography","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":[0.0009585688,0.0001682888,0.0004265528,0.0002983455,0.00009923105,0.00004581791,0.000263789,0.0001183,0.0000484576],"category_scores_gemma":[0.0001536685,0.0001440857,0.0001844223,0.0002946461,0.00008657889,0.0002950573,0.00001164546,0.00008052595,0.000001668273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001243943,"about_ca_system_score_gemma":0.0001681267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003391836,"about_ca_topic_score_gemma":0.000007989306,"domain_scores_codex":[0.9982718,0.0001090368,0.0007006485,0.0001880398,0.0004240153,0.0003064512],"domain_scores_gemma":[0.998796,0.0001299851,0.0002464329,0.0002160634,0.0005117274,0.00009982517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005227809,0.00003348906,0.00005840654,0.00002974248,0.00001352782,0.000007348441,0.0002120821,0.133621,0.865554,0.0001272384,0.00005755686,0.0002332952],"study_design_scores_gemma":[0.0005328316,0.0001406187,0.00003055562,0.00008684942,0.00004388107,0.00001717817,0.0004007893,0.5484607,0.4496044,0.0002762532,0.0002345987,0.0001714123],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.916516,0.0001611868,0.0809278,0.00004542257,0.002183891,0.00005692396,0.00004242725,0.00002864546,0.0000377043],"genre_scores_gemma":[0.9802167,0.00002290993,0.01880976,0.00003447713,0.0008316654,0.000006936684,0.000005928148,0.00003927672,0.00003236093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4159496,"threshold_uncertainty_score":0.5875643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0378224777778965,"score_gpt":0.2614155239991471,"score_spread":0.2235930462212506,"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."}}