{"id":"W2015809374","doi":"10.1007/s11356-014-3511-6","title":"Response surface methodology for the modeling and optimization of oil-in-water emulsion separation using gas sparging assisted microfiltration","year":2014,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Microfiltration; Response surface methodology; Sparging; Volumetric flow rate; Emulsion; Permeation; Chromatography; Air sparging; Flux (metallurgy); Central composite design; Chemistry; Membrane technology; Materials science; Membrane; Thermodynamics; Environmental remediation; Contamination","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":[0.0005476301,0.0007688064,0.0008777948,0.0003161544,0.0003378213,0.0005994141,0.0008420604,0.001073275,0.0009508972],"category_scores_gemma":[0.0006527086,0.0003560224,0.001377388,0.0004245846,0.0002202814,0.0003800388,0.0003225419,0.001002771,0.000250546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005370501,"about_ca_system_score_gemma":0.0006482876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009072413,"about_ca_topic_score_gemma":0.005158427,"domain_scores_codex":[0.9997882,0.00008283768,0.00001193473,0.00002488166,0.00006783141,0.00002436557],"domain_scores_gemma":[0.9997221,0.0001928062,0.00001576519,0.00001159403,0.00005199072,0.000005812169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004719295,0.00009773301,0.0002754827,0.00009938415,0.00004915618,0.00004145694,0.00002695916,0.9723788,0.01635705,0.001059009,0.0001316574,0.009436038],"study_design_scores_gemma":[0.000003149836,0.00002451094,0.00006332572,9.344977e-7,0.000004332755,0.000003034818,0.000003773021,0.9970892,0.0025654,0.0001092374,0.0001305364,0.000002537652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2539047,0.001158577,0.7391763,0.0002207741,0.00006289972,0.0001277105,0.0002398393,0.0009347632,0.004174537],"genre_scores_gemma":[0.9131182,0.0006932874,0.08295866,0.00004471081,0.00001394959,0.000298782,0.0002318002,0.00008827535,0.002552279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009072413,"threshold_uncertainty_score":0.01803923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1251704575092447,"score_gpt":0.3855694717062161,"score_spread":0.2603990141969714,"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."}}