{"id":"W2293537512","doi":"10.1002/bit.25975","title":"Optimization of biomolecule separation by combining microscale filtration and design‐of‐experiment methods","year":2016,"lang":"en","type":"article","venue":"Biotechnology and Bioengineering","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microscale chemistry; Filtration (mathematics); Separation process; Ultrafiltration (renal); Cross-flow filtration; Design of experiments; Biomolecule; Chromatography; Process engineering; Throughput; Ionic strength; Chemistry; Membrane; Biological system; Computer science; Materials science; Nanotechnology; Mathematics; Engineering; Aqueous solution; Statistics","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":[0.0001206526,0.0001131713,0.0001606057,0.0001468988,0.00002498666,0.000005444129,0.00005644466,0.0002868006,0.00000247917],"category_scores_gemma":[0.00002452293,0.00008905459,0.00001359036,0.000140078,0.0001710763,0.00005699989,0.00003651095,0.00005214606,2.208507e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001459913,"about_ca_system_score_gemma":0.000003528505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002141997,"about_ca_topic_score_gemma":9.100865e-8,"domain_scores_codex":[0.9995072,0.00001329608,0.0001968914,0.0001307974,0.00003268326,0.0001190877],"domain_scores_gemma":[0.9997692,0.00004161438,0.00004151617,0.000115621,0.0000164246,0.00001562216],"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":[0.000005289375,0.000007680309,0.0000283142,0.00003189468,0.00002029651,2.555523e-7,0.00002430112,0.0009573667,0.9823852,0.0004022037,0.00009215136,0.01604506],"study_design_scores_gemma":[0.0001962792,0.00008588632,0.0000145346,0.00005313458,0.00001141986,0.0000100245,0.0000379818,0.0415322,0.9576937,0.00004118422,0.0002189175,0.0001047187],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1292232,0.003926111,0.8662431,0.000149591,0.0000587053,0.00009527274,0.000007595105,0.0002905321,0.000005854507],"genre_scores_gemma":[0.8098211,0.002396143,0.1877539,0.000002180177,0.000003232949,0.000003915664,0.000003351541,0.00001038256,0.000005762533],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6805979,"threshold_uncertainty_score":0.3631541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01215929031271292,"score_gpt":0.2556250359958775,"score_spread":0.2434657456831646,"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."}}