{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001857853,0.0001303268,0.0001705004,0.000196039,0.00007378991,0.00001850174,0.0001688699,0.0001162238,0.000006070134],"category_scores_gemma":[0.0002909189,0.0001226658,0.00005742187,0.0003092275,0.00003055758,0.00004966701,0.00002366153,0.00008255513,0.000001373809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001306045,"about_ca_system_score_gemma":0.00004539981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002484304,"about_ca_topic_score_gemma":0.00001051314,"domain_scores_codex":[0.9992403,0.00001494609,0.0002821111,0.0001516055,0.0001028205,0.0002082391],"domain_scores_gemma":[0.9994059,0.00005857701,0.00004610902,0.0002389557,0.0002378939,0.00001256471],"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":[0.00004244326,0.00001325596,0.000007271882,0.00008630944,0.00005094658,5.163477e-7,0.00001474283,0.001026402,0.9145076,0.0003626931,0.06281915,0.02106863],"study_design_scores_gemma":[0.0002288105,0.00006277197,0.00003435953,0.0001045592,0.00003078972,0.000001272494,0.00001741158,0.04025707,0.9392242,0.003806696,0.01612047,0.0001116161],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0955627,0.006463281,0.8956295,0.0009773206,0.0001880339,0.0005859093,0.00001355325,0.000438111,0.000141591],"genre_scores_gemma":[0.7813594,0.0008011414,0.2173122,0.00007082259,0.00007743857,0.0000959781,0.00001297071,0.00003194945,0.0002381262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6857967,"threshold_uncertainty_score":0.5002167,"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."}}