{"id":"W1988839985","doi":"10.1002/biot.200800331","title":"Scale‐up of controlled‐shear affinity filtration using computational fluid dynamics","year":2009,"lang":"en","type":"article","venue":"Biotechnology Journal","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"","keywords":"Computational fluid dynamics; Filtration (mathematics); Bioprocess; Bioreactor; Chromatography; Rotor (electric); SCALE-UP; Fluid dynamics; Turbulence; Scale (ratio); Cross-flow filtration; Mechanics; Chemistry; Materials science; Membrane; Biological system; Mechanical engineering; Engineering; Chemical engineering; Physics; Mathematics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002306599,0.0001585707,0.0003453279,0.0003520672,0.0001307318,0.00002632851,0.0002190778,0.0005387943,0.00001652577],"category_scores_gemma":[0.00008059021,0.0001281388,0.0001047872,0.0002594664,0.0002040414,0.00008906916,0.00002389736,0.0005119992,0.000005035489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350704,"about_ca_system_score_gemma":0.00004693701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002350102,"about_ca_topic_score_gemma":0.000002160144,"domain_scores_codex":[0.9990282,0.00002346659,0.0004409693,0.0001189316,0.000143883,0.0002445579],"domain_scores_gemma":[0.9995388,0.00003465957,0.0001273799,0.0001682936,0.00009616045,0.00003472063],"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":[0.0001432049,0.000102885,0.000380283,0.00002191936,0.0001228168,0.00002964675,0.00006316484,0.01778851,0.8898883,0.005008643,0.003490793,0.08295985],"study_design_scores_gemma":[0.00380673,0.0004286708,0.002181731,0.0001252213,0.00009071598,0.002058518,0.0004062501,0.5044538,0.4663491,0.0191278,0.0005250967,0.0004463067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8006045,0.0007624521,0.1960265,0.001488688,0.0003771274,0.0001032475,0.00001131953,0.0005117242,0.0001144578],"genre_scores_gemma":[0.970492,0.0004129354,0.0289808,0.00003527031,0.00005000293,3.712603e-7,0.000006516482,0.00001134394,0.00001073132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4866654,"threshold_uncertainty_score":0.522535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01225367591412066,"score_gpt":0.2250974120023351,"score_spread":0.2128437360882144,"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."}}