{"id":"W3086856639","doi":"10.1101/2020.09.11.292730","title":"In silico prediction of COVID-19 test efficiency with DinoKnot","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Primer (cosmetics); In silico; Computational biology; Nucleic acid; RNA; Genome; Reverse transcriptase; Biology; Primer binding site; Coronavirus; Genetics; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); DNA; Duplex (building); Coronavirus disease 2019 (COVID-19); Gene; Chemistry; Medicine; Infectious disease (medical specialty)","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":[0.002034798,0.001433855,0.001049446,0.0006605769,0.0002998144,0.0006822642,0.001051485,0.0008604435,0.003335458],"category_scores_gemma":[0.004486762,0.0005845972,0.0009863479,0.0002454181,0.0002962055,0.0003748503,0.0004211533,0.0006761391,0.0007698503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000659991,"about_ca_system_score_gemma":0.0009904263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001618974,"about_ca_topic_score_gemma":0.002382648,"domain_scores_codex":[0.9992412,0.0003081794,0.00005651104,0.0001886457,0.0001337061,0.00007188112],"domain_scores_gemma":[0.996157,0.003062177,0.0002525164,0.0001053503,0.0002767247,0.0001463897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003357548,0.0005692123,0.04491743,0.000769841,0.000351768,0.0004765343,0.00009297451,0.8758429,0.04165425,0.001521807,0.00178646,0.02865934],"study_design_scores_gemma":[0.00008508455,0.0003299782,0.001246089,0.00001250846,0.00007707566,0.00006386251,0.000009482761,0.9820464,0.01486037,0.0003077081,0.0009447638,0.00001667762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7339199,0.0005842951,0.2474947,0.000201256,0.00009993948,0.0003781584,0.001983167,0.01235332,0.002985284],"genre_scores_gemma":[0.8329922,0.0001647439,0.1604753,0.0001653083,0.00001381564,0.0004594594,0.003837197,0.0006830652,0.001208838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003335458,"threshold_uncertainty_score":0.01115817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01491428962519422,"score_gpt":0.2236605147936616,"score_spread":0.2087462251684673,"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."}}