{"id":"W2091622791","doi":"10.1007/s10404-010-0702-4","title":"On-chip PCR amplification of genomic and viral templates in unprocessed whole blood","year":2010,"lang":"en","type":"article","venue":"Microfluidics and Nanofluidics","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Blood Services; University of Alberta","funders":"Canada Research Chairs","keywords":"Microfluidics; Polymerase chain reaction; genomic DNA; Applications of PCR; DNA; Real-time polymerase chain reaction; Lab-on-a-chip; DNA extraction; Whole blood; Multiple displacement amplification; Recombinase Polymerase Amplification; Computational biology; Molecular biology; Biology; Digital polymerase chain reaction; Gene; Nanotechnology; Materials science; Genetics; Immunology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002731944,0.0002751267,0.0003388263,0.0002888577,0.00008733774,0.00004872789,0.0001910709,0.0003454222,0.000008285536],"category_scores_gemma":[0.00005926005,0.0002605881,0.00004037105,0.0002193141,0.000289452,0.00007746813,0.00006356395,0.0003828955,0.000009062176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001635653,"about_ca_system_score_gemma":0.00003206293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003870302,"about_ca_topic_score_gemma":0.000007735055,"domain_scores_codex":[0.9987798,0.00001502744,0.0004320693,0.0003311701,0.0001194193,0.0003225293],"domain_scores_gemma":[0.9993839,0.00009853346,0.00006260441,0.0003304909,0.00005136595,0.0000730778],"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":[0.0000177636,0.00005740528,0.002915323,0.0001074526,0.00002184262,0.00000565472,0.0001714127,8.457364e-7,0.9885283,0.002598651,0.00159448,0.003980899],"study_design_scores_gemma":[0.0007263225,0.0001114052,0.006582546,0.00005973435,0.0000309172,0.00003841818,0.00008621105,0.0001869715,0.9826699,0.002273779,0.006931203,0.0003025788],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830313,0.01570743,0.0003444382,0.000144882,0.0001878057,0.0001908213,0.00005317412,0.0001954058,0.0001447182],"genre_scores_gemma":[0.9862537,0.01277933,0.0007716738,0.00005821675,0.00003429395,0.000007230841,0.00002321443,0.00004248704,0.00002981847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005858357,"threshold_uncertainty_score":0.9999846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006414473465598538,"score_gpt":0.1894753624214139,"score_spread":0.1830608889558154,"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."}}