{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007643619,0.0005413675,0.0005492818,0.0003211269,0.0002334695,0.0007064911,0.0005703543,0.000506409,0.001958495],"category_scores_gemma":[0.001081877,0.0005250145,0.0005293579,0.0002901249,0.0002913767,0.0002837478,0.0002880091,0.0006929563,0.001317087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004147905,"about_ca_system_score_gemma":0.0007061976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000895858,"about_ca_topic_score_gemma":0.002208617,"domain_scores_codex":[0.9992041,0.0001125325,0.00004594609,0.0003210058,0.0001560854,0.0001603613],"domain_scores_gemma":[0.9993937,0.0004235146,0.00002717233,0.00006373747,0.00005747283,0.0000344397],"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":[0.0002480785,0.0000749011,0.0002555251,0.00005065753,0.00001488579,0.00002586894,0.00004912757,0.0003026534,0.9935513,0.0001544477,0.0001907835,0.00508173],"study_design_scores_gemma":[0.00001427281,0.0001334981,0.0008357565,0.0000049771,0.00001799845,0.00003577844,0.00001481332,0.003437016,0.9945274,0.00005498034,0.0009183319,0.000005258541],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7941012,0.001790269,0.1950313,0.0002782233,0.0003463704,0.0004150674,0.001826267,0.001344232,0.004867106],"genre_scores_gemma":[0.7550334,0.001333797,0.2216864,0.0006478513,0.0001129715,0.0008297734,0.00567529,0.000362399,0.01431815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001958495,"threshold_uncertainty_score":0.006551802,"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."}}