{"id":"W4412463136","doi":"10.2139/ssrn.5353341","title":"Synthesis Feature-Coupled Machine Learning Approaches to Predict the Capacitance of Biomass-Derived Carbon Electrodes in Supercapacitor","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Supercapacitor Materials and Fabrication","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Supercapacitor; Capacitance; Feature (linguistics); Biomass (ecology); Carbon fibers; Electrode; Materials science; Process engineering; Nanotechnology; Computer science; Chemistry; Engineering; Composite material; Biology; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003135516,0.0005842313,0.0005228225,0.0004466833,0.0003150991,0.0005469835,0.0006903612,0.0009457888,0.001536685],"category_scores_gemma":[0.001338155,0.0004024557,0.0005078045,0.0004989274,0.0003282784,0.0005096775,0.0004172511,0.0007016149,0.000278005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004839491,"about_ca_system_score_gemma":0.0004845735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004408286,"about_ca_topic_score_gemma":0.005587002,"domain_scores_codex":[0.9999186,0.00001594824,0.000004957454,0.00002660834,0.00002165331,0.00001212615],"domain_scores_gemma":[0.9996004,0.0002444222,0.0000259054,0.00002981574,0.00008726212,0.00001232851],"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":[0.00006716375,0.00005553661,0.000611415,0.00006309245,0.00004212156,0.00006118858,0.00002073301,0.9335157,0.01301893,0.001961685,0.0003731581,0.05020934],"study_design_scores_gemma":[0.000001215581,0.000004665791,0.00006219981,7.532884e-7,0.000002028095,0.000002254835,8.978109e-7,0.9984279,0.001008186,0.0004495147,0.00003921262,0.000001262185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1616605,0.0004968871,0.8333298,0.0002965982,0.00006078395,0.00006911089,0.0002347426,0.0007863151,0.003065138],"genre_scores_gemma":[0.9094971,0.0001532532,0.08788677,0.00005829535,0.0000355674,0.00008372377,0.0002400294,0.00005792681,0.001987194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004408286,"threshold_uncertainty_score":0.00876528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02670844545503641,"score_gpt":0.2247216526925381,"score_spread":0.1980132072375017,"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."}}