{"id":"W2117216570","doi":"10.1007/bf03326078","title":"Modeling of a permeate flux of cross-flow membrane filtration of colloidal suspensions: A wavelet network approach","year":2009,"lang":"en","type":"article","venue":"International Journal of Environmental Science and Technology","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Wavelet; Mean squared error; Artificial neural network; Backpropagation; Feedforward neural network; Filtration (mathematics); Computer science; Artificial intelligence; Mathematics; Statistics","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":[0.0004588025,0.0006136582,0.000674634,0.0003410527,0.0003351066,0.0008172627,0.0006976836,0.00207941,0.0007105641],"category_scores_gemma":[0.001067026,0.0004578413,0.000792484,0.0003143307,0.0006291608,0.001055517,0.0004264265,0.0007158802,0.0001231821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007686518,"about_ca_system_score_gemma":0.0007100847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009889254,"about_ca_topic_score_gemma":0.002965919,"domain_scores_codex":[0.9998754,0.0000405476,0.000005581895,0.00003379592,0.00002665809,0.00001789946],"domain_scores_gemma":[0.9996967,0.0001827957,0.00003687512,0.00001100058,0.00005330657,0.00001938492],"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":[0.00001939342,0.00001889938,0.0002307485,0.00001926424,0.00001234043,0.00004176955,0.00001747914,0.9917389,0.002998605,0.00305076,0.0000696277,0.001782153],"study_design_scores_gemma":[6.603836e-7,0.000002268925,0.00002697906,4.570881e-7,0.000001164619,0.000001634351,0.000001351493,0.9997073,0.0001213054,0.0001204104,0.00001545095,9.394532e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2184178,0.0004205741,0.7757415,0.0004881359,0.00006564596,0.00003694269,0.0001013594,0.00015692,0.004571103],"genre_scores_gemma":[0.9722131,0.0004481123,0.02152931,0.00005854274,0.00003342967,0.00007459165,0.0001000693,0.00005290475,0.005489896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009889254,"threshold_uncertainty_score":0.01966339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005767857529981294,"score_gpt":0.215702850006375,"score_spread":0.2099349924763937,"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."}}