{"id":"W4243484763","doi":"10.21203/rs.3.rs-89445/v1","title":"A Multiplexed, Next Generation Sequencing Platform for High-Throughput Detection of SARS-CoV-2","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"Krembil Foundation; University of Toronto","keywords":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Throughput; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Computer science; Multiplexing; DNA sequencing; Sars virus; Computational biology; Virology; Biology; Medicine; Operating system; Genetics; Telecommunications; Gene; Internal medicine; Outbreak; Infectious disease (medical specialty)","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":[0.00139195,0.0003571595,0.0007189742,0.0006826626,0.0003288264,0.0001365119,0.0002271843,0.0006099467,0.00001183387],"category_scores_gemma":[0.004390906,0.0003540119,0.000335273,0.0006995798,0.0001463747,0.0001963447,0.0004277331,0.00176415,0.00004145728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001113636,"about_ca_system_score_gemma":0.0009160174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002696874,"about_ca_topic_score_gemma":0.0006607389,"domain_scores_codex":[0.9962952,0.0001758523,0.0007723101,0.0009303921,0.001182161,0.0006441203],"domain_scores_gemma":[0.996702,0.000554076,0.0003165686,0.0007005543,0.001620804,0.0001060222],"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":[0.0004558027,0.00006876946,0.0001002844,0.002938077,0.0001257716,0.00002939368,0.0007077993,0.0000569958,0.9767028,0.0001204799,0.0003093476,0.01838449],"study_design_scores_gemma":[0.001667917,0.0008438139,0.0001294784,0.0009183802,0.00006795438,0.00004044414,0.0005099447,0.08860024,0.9045528,0.001123733,0.001296134,0.0002492145],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759839,0.0004072866,0.01826103,0.0004031732,0.0006396613,0.003556013,0.0000940087,0.000291369,0.0003635544],"genre_scores_gemma":[0.9876295,0.00004904795,0.009340831,0.0002689019,0.001719862,0.000615149,0.0002291171,0.0001110595,0.00003647896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08854324,"threshold_uncertainty_score":0.9998912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4450691429376838,"score_gpt":0.4481476457896884,"score_spread":0.003078502852004616,"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."}}