{"id":"W3170045849","doi":"10.1111/ijac.13824","title":"Innovative in situ investigations using synchrotron‐based micro tomography and molecular dynamics simulation for fouling assessment in ceramic membranes for dairy and food industry","year":2021,"lang":"en","type":"article","venue":"International Journal of Applied Ceramic Technology","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada); Toronto Metropolitan University; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation; University of Saskatchewan; Canadian Light Source","keywords":"Membrane; Fouling; Microfiltration; Materials science; Ceramic; Membrane fouling; Ceramic membrane; Filtration (mathematics); Porosity; Synchrotron; Chemical engineering; Membrane structure; Composite material; Chromatography; Chemistry; Mathematics; Optics","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":[0.0002651162,0.0001480845,0.0002412658,0.0007779957,0.00004967243,0.00004412804,0.0002352356,0.0003458207,0.000007012347],"category_scores_gemma":[0.0001737772,0.0001642608,0.00003688553,0.0007006893,0.0002715116,0.0001683996,0.0001295859,0.000478009,1.264051e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005573031,"about_ca_system_score_gemma":0.0001427345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006929553,"about_ca_topic_score_gemma":0.0001243941,"domain_scores_codex":[0.9987575,0.00001898848,0.0005689848,0.0002768595,0.0001952305,0.0001823757],"domain_scores_gemma":[0.9991418,0.0001966072,0.0003815311,0.0001136439,0.000139751,0.0000266527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001186252,0.0001384014,0.02809943,0.00005599497,0.00009884134,0.00001886065,0.0001817604,0.257183,0.6851759,0.01504106,0.000003909025,0.01388428],"study_design_scores_gemma":[0.006743679,0.0002661799,0.01028536,0.0001715998,0.00004213532,0.0000876545,0.002302657,0.529651,0.3102499,0.1396022,0.0001886243,0.0004090803],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9181999,0.00006642903,0.07921617,0.001857792,0.00008087073,0.0004446512,0.00002016189,0.0000255319,0.00008850843],"genre_scores_gemma":[0.920426,0.00001264511,0.07924701,0.0002137231,0.00001402285,0.00004670097,0.00002368218,0.00001463663,0.00000156649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.374926,"threshold_uncertainty_score":0.6698359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685354484483468,"score_gpt":0.3005982226663076,"score_spread":0.2837446778214729,"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."}}