{"id":"W3206585822","doi":"10.1109/ichi52183.2021.00082","title":"In silico prediction of COVID-19 test efficiency with DinoKnot","year":2021,"lang":"en","type":"article","venue":"","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Victoria","funders":"","keywords":"Primer (cosmetics); In silico; Primer binding site; Nucleic acid; Computational biology; RNA; Biology; Duplex (building); Genome; Reverse transcriptase; DNA; Genetics; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Gene; Chemistry; Medicine; Infectious disease (medical specialty); Disease","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":[0.002072809,0.001449879,0.001076666,0.0005539992,0.0002927841,0.0007573698,0.0009842489,0.0008302215,0.003225117],"category_scores_gemma":[0.004134384,0.0005854694,0.0009834999,0.0002436648,0.0002913739,0.0003779462,0.0004426706,0.0007477443,0.0007993215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000625795,"about_ca_system_score_gemma":0.001044175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524039,"about_ca_topic_score_gemma":0.00284559,"domain_scores_codex":[0.9992113,0.0003035988,0.00006655292,0.0002046088,0.0001382091,0.00007566821],"domain_scores_gemma":[0.9961236,0.003179193,0.000236653,0.00009961159,0.0002403756,0.0001205951],"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":[0.004095026,0.0007269282,0.04592758,0.001191442,0.0004705484,0.0005252822,0.0001627436,0.8312354,0.07416372,0.001862091,0.002093716,0.03754548],"study_design_scores_gemma":[0.0001329208,0.0005996664,0.001810502,0.00001947054,0.0001303973,0.0000904243,0.00001723674,0.9636741,0.03145861,0.0004508079,0.001589034,0.00002678886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7731165,0.0006339267,0.2098265,0.0002040468,0.00008546045,0.0003803511,0.002184311,0.01023721,0.003331671],"genre_scores_gemma":[0.8078265,0.000281801,0.1832948,0.0001934197,0.00001435684,0.0005395721,0.005486538,0.0008202433,0.001542934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003225117,"threshold_uncertainty_score":0.01096219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01173309577627207,"score_gpt":0.2416047778265528,"score_spread":0.2298716820502807,"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."}}