{"id":"W4402686982","doi":"10.3390/membranes14090199","title":"A Novel Modeling Optimization Approach for a Seven-Channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning","year":2024,"lang":"en","type":"article","venue":"Membranes","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ultrafiltration (renal); Factorial experiment; Filtration (mathematics); Permeation; Fractional factorial design; Membrane; Linear regression; Membrane technology; Materials science; Chromatography; Mathematics; Statistics; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004691141,0.0002364676,0.000208785,0.0001916738,0.0001654144,0.0001998957,0.0001396963,0.0001506672,0.00005749773],"category_scores_gemma":[0.0000463898,0.0002115853,0.00003733441,0.000259618,0.00004405565,0.0005840386,0.00002994714,0.0001507269,0.000007484257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001948513,"about_ca_system_score_gemma":0.00001824449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009801666,"about_ca_topic_score_gemma":0.00002508983,"domain_scores_codex":[0.9984525,0.0001135593,0.0003434361,0.0005734287,0.0002768975,0.0002401652],"domain_scores_gemma":[0.9995837,0.000105719,0.00006301882,0.000180148,0.00001595953,0.00005151367],"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":[0.0001843522,0.00005019837,0.000009784614,0.0001874908,0.000004696993,0.000001247501,0.0009380588,0.7644781,0.2339666,0.00005548273,0.000002531794,0.0001214073],"study_design_scores_gemma":[0.000803662,0.0002524267,0.000002694711,0.00005698766,0.00001472422,0.000008349935,0.0006891707,0.9229524,0.0749696,0.00002017142,0.000008854196,0.0002210256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1316784,0.00006147441,0.8664484,0.00006146143,0.0001774503,0.001024247,0.00001335182,0.0003623259,0.0001729087],"genre_scores_gemma":[0.9320405,0.00000846914,0.0672189,0.00002228765,0.00005707434,0.0003515954,0.0001920199,0.00004208522,0.00006707893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8003621,"threshold_uncertainty_score":0.86282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0388816983482741,"score_gpt":0.2523622771484145,"score_spread":0.2134805788001404,"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."}}