{"id":"W2003180322","doi":"10.1002/elps.201000098","title":"Microfluidic devices for electrokinetic sample fractionation","year":2010,"lang":"en","type":"article","venue":"Electrophoresis","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; National Institute for Nanotechnology; University of Alberta","funders":"","keywords":"Electrokinetic phenomena; Fractionation; Analyte; Microfluidics; Contamination; Chromatography; Sample (material); Throughput; Sample preparation; Materials science; Chip; Analytical Chemistry (journal); Nanotechnology; Chemistry; Computer science","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":[0.0004711225,0.0006479964,0.0003885345,0.000499821,0.0003503997,0.0006916496,0.0009107522,0.0005304999,0.001237982],"category_scores_gemma":[0.000558552,0.0003878655,0.0002948986,0.000276356,0.0004156988,0.0005840849,0.0005443461,0.0005427584,0.0005872633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005405186,"about_ca_system_score_gemma":0.0003543242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00015848,"about_ca_topic_score_gemma":0.0002498513,"domain_scores_codex":[0.9996105,0.00005253549,0.00003215824,0.0000861759,0.0001832781,0.00003533348],"domain_scores_gemma":[0.9997863,0.00008068211,0.00004150035,0.00003861259,0.00003873061,0.00001421491],"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":[0.00008371459,0.00004296803,0.0003925082,0.0004696664,0.00004162623,0.00007532477,0.00008656197,0.001168243,0.9089683,0.009800505,0.002453132,0.07641748],"study_design_scores_gemma":[0.00003384922,0.0002139047,0.0009657231,0.00004915629,0.00005125763,0.0006389573,0.00001606467,0.006164399,0.8955858,0.002644676,0.09359124,0.00004509441],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07236738,0.02470606,0.8897797,0.0008940098,0.000911544,0.0004425049,0.0006943638,0.002559712,0.007644731],"genre_scores_gemma":[0.3596798,0.01565231,0.6116368,0.0009218904,0.0003536658,0.0006778455,0.0006235898,0.0001185001,0.01033562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001237982,"threshold_uncertainty_score":0.00414151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004614153332405842,"score_gpt":0.2094999156149329,"score_spread":0.2048857622825271,"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."}}