{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000225388,0.0001994952,0.0002042769,0.0001691138,0.0002441816,0.0002839976,0.0002650801,0.0003504521,0.0004918145],"category_scores_gemma":[0.0002337076,0.0001834218,0.0003214289,0.0002320704,0.0002034452,0.0003411649,0.0001886521,0.0002555109,0.00004731863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003691225,"about_ca_system_score_gemma":0.0004756054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003028808,"about_ca_topic_score_gemma":0.002765263,"domain_scores_codex":[0.9999471,0.000008684719,0.000003205376,0.000009267814,0.00002381651,0.000007895086],"domain_scores_gemma":[0.9999131,0.00003059405,0.0000157645,0.00000941934,0.00002439303,0.000006758329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001134619,0.0001729417,0.009485599,0.0001817384,0.00004272037,0.0001644846,0.000163201,0.2489284,0.727767,0.002078821,0.000190858,0.01071073],"study_design_scores_gemma":[0.00001003298,0.00007934397,0.002804552,0.000004182208,0.00001113227,0.00003273713,0.00007519232,0.9019897,0.09418928,0.0002513424,0.0005421393,0.00001037267],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9547952,0.0001744526,0.04402337,0.00009048382,0.00001233858,0.00001983526,0.00008781098,0.0001013413,0.0006952561],"genre_scores_gemma":[0.9765862,0.0001499643,0.02283028,0.000008086347,0.000002395182,0.00002362121,0.00005136112,0.000008389439,0.0003395854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003028808,"threshold_uncertainty_score":0.006022334,"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."}}