{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001743453,0.0007887945,0.0005654179,0.0007428883,0.0004191214,0.0008980955,0.0005139151,0.0007482251,0.0019707],"category_scores_gemma":[0.001511683,0.0004719507,0.0005870804,0.0003865281,0.0003899158,0.0004727289,0.0006812513,0.0009105739,0.0008502403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005289012,"about_ca_system_score_gemma":0.0008015781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001361136,"about_ca_topic_score_gemma":0.002683,"domain_scores_codex":[0.9984798,0.0002878138,0.00008064325,0.0006064989,0.0004615415,0.00008357214],"domain_scores_gemma":[0.9992769,0.0002102795,0.0001338494,0.00009273945,0.0001998305,0.00008623172],"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":[0.001152615,0.0003003408,0.008485643,0.0002877678,0.0002437077,0.0002059286,0.000170874,0.008187161,0.9280836,0.0009981358,0.003736075,0.04814812],"study_design_scores_gemma":[0.000217209,0.002384336,0.02257578,0.00008879606,0.000270818,0.0008732195,0.0001159339,0.1777939,0.7639487,0.001889989,0.02961067,0.0002305366],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.568754,0.003342435,0.3914128,0.0009152439,0.000809807,0.001770606,0.01958346,0.00846135,0.00495036],"genre_scores_gemma":[0.6165892,0.0008986745,0.3634028,0.001176362,0.0001978204,0.001304339,0.01132119,0.0003530532,0.004756473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0019707,"threshold_uncertainty_score":0.009220362,"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."}}