{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004818874,0.0001251633,0.0001618334,0.00005249814,0.00003433105,0.00003144192,0.00007698969,0.00007543547,0.000001398502],"category_scores_gemma":[0.000562957,0.0001211174,0.00003479451,0.0001206007,0.00002062049,0.00005132339,0.00001539183,0.000105691,0.000008803281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001266302,"about_ca_system_score_gemma":0.000007676556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000162655,"about_ca_topic_score_gemma":6.693091e-7,"domain_scores_codex":[0.9994633,0.000006783249,0.0001387461,0.0001622591,0.00005947685,0.0001694343],"domain_scores_gemma":[0.9993601,0.0004676289,0.00002793586,0.00009429967,0.00001382863,0.0000361412],"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":[0.00004149968,0.00003829384,0.0006328669,0.000221347,0.00008335657,0.000004910424,0.0003682112,0.00004086602,0.9751665,0.0007013943,0.006118635,0.0165821],"study_design_scores_gemma":[0.0005076372,0.0004658272,0.000152636,0.0001089976,0.00003656355,0.000004117842,0.00003224701,0.0003760267,0.9935179,0.00006320808,0.0046028,0.0001320303],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99437,0.00282492,0.0003479279,0.0002695263,0.00004429449,0.0001963693,0.00005155165,0.0005053941,0.001390031],"genre_scores_gemma":[0.9992278,0.0004264553,0.000122958,0.0001016006,0.00003859038,0.00002465668,0.00001353869,0.00002552889,0.00001891686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01835139,"threshold_uncertainty_score":0.4939026,"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."}}