{"id":"W3019638927","doi":"10.1039/d0lc00302f","title":"Direct loading of blood for plasma separation and diagnostic assays on a digital microfluidic device","year":2020,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Public Health","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institutes of Health Research; Abbott Laboratories","keywords":"Microfluidics; Separation (statistics); Plasma; Digital polymerase chain reaction; Materials science; Nanotechnology; Chromatography; Chemistry; Computer science; Physics; Biochemistry","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":[0.0003082111,0.0004051906,0.0002712081,0.0003499111,0.000204104,0.0004599616,0.000430344,0.0003545942,0.001865654],"category_scores_gemma":[0.0008831889,0.0001669496,0.0001876383,0.0001412298,0.000323406,0.0003069034,0.0004636094,0.0003092793,0.0007994724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003082396,"about_ca_system_score_gemma":0.0003508035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002169073,"about_ca_topic_score_gemma":0.0003262702,"domain_scores_codex":[0.9997178,0.00003057486,0.0000215565,0.0001096377,0.00009130136,0.00002908817],"domain_scores_gemma":[0.9997559,0.0001143237,0.00005173696,0.0000299927,0.00002993814,0.00001814914],"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":[0.00005399987,0.00002731618,0.0004676626,0.0001117359,0.000007205044,0.00004545007,0.00002584153,0.00007770554,0.9866118,0.000357161,0.0004726358,0.01174143],"study_design_scores_gemma":[0.00001219672,0.0001353101,0.001481914,0.00001387683,0.00001342985,0.0002052316,0.00001036378,0.001206463,0.9885821,0.0001456087,0.008183253,0.00001019243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.591203,0.006028852,0.3837313,0.001203962,0.0009435236,0.0008560407,0.002842361,0.003170248,0.01002069],"genre_scores_gemma":[0.699617,0.002919967,0.2848354,0.001122274,0.0003139418,0.0007472486,0.001580882,0.000139311,0.008723945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001865654,"threshold_uncertainty_score":0.006241262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01249108995349255,"score_gpt":0.2217310211731726,"score_spread":0.2092399312196801,"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."}}