{"id":"W3169986404","doi":"10.1016/j.apenergy.2021.117177","title":"A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries","year":2021,"lang":"en","type":"article","venue":"Applied Energy","topic":"Advanced battery technologies research","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Research Grants Council, University Grants Committee","keywords":"Electrode; Porosity; Genetic algorithm; Computer science; Materials science; Permeability (electromagnetism); Algorithm; Artificial intelligence; Biological system; Machine learning; Composite material; Chemistry","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":[0.0004848703,0.0003783526,0.0005934892,0.0005545452,0.0002988404,0.0006952285,0.001047574,0.001162927,0.001017069],"category_scores_gemma":[0.001225811,0.0003859632,0.000574035,0.0004352908,0.0005176572,0.0004518274,0.000540678,0.0006145121,0.0001592356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004851179,"about_ca_system_score_gemma":0.0007497043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002364096,"about_ca_topic_score_gemma":0.002899108,"domain_scores_codex":[0.9998258,0.00005245217,0.000008640577,0.00002336864,0.00007289024,0.00001691819],"domain_scores_gemma":[0.9997036,0.0001878628,0.00002535998,0.00001114122,0.00006115699,0.00001080312],"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":[0.00002511425,0.00003930784,0.0001722393,0.00004292264,0.00002198968,0.00002921525,0.00001886072,0.9696847,0.004736748,0.004124804,0.0001242981,0.0209798],"study_design_scores_gemma":[0.000005241649,0.00002016611,0.00003140159,0.000002489305,0.00000347014,0.000005658188,0.000002342144,0.9982413,0.0006639115,0.0008399194,0.0001818505,0.000002311276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03751549,0.0004285601,0.9577949,0.000193877,0.00004243036,0.00005982739,0.00002354935,0.000179238,0.003762235],"genre_scores_gemma":[0.5915627,0.0004706256,0.4045091,0.000111604,0.0000384751,0.0002004719,0.00005545653,0.00006438508,0.00298712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002364096,"threshold_uncertainty_score":0.004700661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126295176686195,"score_gpt":0.2210967806818483,"score_spread":0.2098338289149863,"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."}}