{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003193135,0.0004469367,0.0005727696,0.0001985509,0.0003547117,0.0006206077,0.0006117559,0.0005322879,0.0008582801],"category_scores_gemma":[0.0006812071,0.0002727307,0.0006385348,0.0001742945,0.0003597618,0.0004975109,0.0003872038,0.0005685149,0.000140476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008959536,"about_ca_system_score_gemma":0.001104599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004419318,"about_ca_topic_score_gemma":0.002726323,"domain_scores_codex":[0.999903,0.00001535451,0.000006438712,0.00001864875,0.00003898978,0.00001764947],"domain_scores_gemma":[0.9997329,0.0001501898,0.00002675259,0.00003518802,0.00003616099,0.00001887086],"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":[0.0001510637,0.0001587911,0.001899274,0.000143191,0.00004940014,0.0001066393,0.00007433088,0.8769503,0.09349854,0.003590629,0.0006037675,0.02277406],"study_design_scores_gemma":[0.00002423536,0.00004050044,0.000247092,0.000002606088,0.000007085502,0.000009241288,0.000005107044,0.9780619,0.02059804,0.0003224433,0.0006721748,0.000009588337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7218164,0.0004458312,0.2712589,0.0004029601,0.0001058445,0.0002433501,0.0003360229,0.0009281178,0.004462747],"genre_scores_gemma":[0.8700457,0.0002731418,0.1285332,0.0000305164,0.00001059958,0.0001990986,0.0002412567,0.00005555762,0.0006110345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004419318,"threshold_uncertainty_score":0.008787215,"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."}}