{"id":"W3175708866","doi":"10.1016/j.diagmicrobio.2021.115458","title":"Extractionless nucleic acid detection: a high capacity solution to COVID-19 testing","year":2021,"lang":"en","type":"article","venue":"Diagnostic Microbiology and Infectious Disease","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Multiplex; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); False positive paradox; Nucleic acid; Nucleic Acid Amplification Tests; Detection limit; 2019-20 coronavirus outbreak; Real-time polymerase chain reaction; Computational biology; Multiplex polymerase chain reaction; Genome; Virology; Biology; Gene; Polymerase chain reaction; Chromatography; Chemistry; Medicine; Bioinformatics; Computer science; Genetics","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":[0.003860665,0.002735053,0.001044223,0.002476957,0.0008563069,0.001409439,0.002644133,0.002828641,0.007724306],"category_scores_gemma":[0.00579755,0.002228273,0.00126395,0.0008770988,0.001419849,0.001447071,0.002104918,0.003649002,0.0093763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004571078,"about_ca_system_score_gemma":0.001053036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002272407,"about_ca_topic_score_gemma":0.0007730747,"domain_scores_codex":[0.9907787,0.003158168,0.0008478643,0.001560182,0.003268186,0.0003868179],"domain_scores_gemma":[0.9959906,0.001874098,0.0005727387,0.0006484931,0.0007114517,0.0002026539],"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":[0.0001436248,0.0001856399,0.000751242,0.000393857,0.00004310903,0.000230054,0.0001212772,0.0003728905,0.9685714,0.0005913881,0.001525243,0.02707027],"study_design_scores_gemma":[0.00006649222,0.0007235063,0.001384804,0.0001177937,0.00006514367,0.001833981,0.00005377981,0.003582824,0.9597609,0.0007924402,0.03148582,0.0001324794],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02073697,0.002352631,0.9668825,0.0006736823,0.0007822696,0.002806376,0.0007570828,0.002463325,0.002545197],"genre_scores_gemma":[0.04733337,0.002450425,0.934745,0.001193088,0.0003797244,0.004071185,0.00253017,0.0005380203,0.006759052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007724306,"threshold_uncertainty_score":0.0258404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03295532599995305,"score_gpt":0.2790148454394678,"score_spread":0.2460595194395147,"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."}}