{"id":"W4407693540","doi":"10.1002/elps.8106","title":"Design Guidelines of Free‐Flow Counterflow Gradient Focusing Device for Protein Fractionation","year":2025,"lang":"en","type":"article","venue":"Electrophoresis","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Centres of Excellence","keywords":"Fractionation; Fabrication; Flow (mathematics); Polymethyl methacrylate; Computation; Computer science; Resolution (logic); Construct (python library); Materials science; Chip; Work (physics); Chromatography; Analytical Chemistry (journal); Nanotechnology; Chemistry; Mechanics; Mechanical engineering; Algorithm; Engineering; Physics; Artificial intelligence; Composite material","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":[0.00139634,0.001282729,0.0005715992,0.0007405222,0.0005523997,0.0007607214,0.001411499,0.001001117,0.001727493],"category_scores_gemma":[0.001109978,0.0006649462,0.0003754066,0.0002917581,0.0003346679,0.0005703222,0.0004542499,0.0005576509,0.001822617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00086033,"about_ca_system_score_gemma":0.001464046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000810857,"about_ca_topic_score_gemma":0.001346302,"domain_scores_codex":[0.9992937,0.00008292987,0.00008403811,0.0001126465,0.0003733259,0.00005344376],"domain_scores_gemma":[0.9995626,0.00007786871,0.00007823724,0.00005210035,0.0002015001,0.00002763943],"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":[0.0001559349,0.000104324,0.0006540082,0.001077059,0.0000305968,0.0003298307,0.0002498688,0.01964946,0.9045784,0.02063997,0.00460487,0.04792574],"study_design_scores_gemma":[0.0001122126,0.000502533,0.001162376,0.0001355369,0.00006181731,0.0005007457,0.00009216462,0.133327,0.7522216,0.003253658,0.1085211,0.000109312],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02690001,0.002087506,0.9578865,0.000496847,0.0002738713,0.001563264,0.0005336326,0.001408403,0.008850042],"genre_scores_gemma":[0.09011851,0.001691326,0.9008829,0.0001819649,0.00005052135,0.003015069,0.000529438,0.0001481417,0.003382087],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001727493,"threshold_uncertainty_score":0.007384658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02379433203020385,"score_gpt":0.2547908917722371,"score_spread":0.2309965597420332,"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."}}