{"id":"W3125805574","doi":"10.1038/s41598-020-80352-8","title":"Direct detection of SARS-CoV-2 using non-commercial RT-LAMP reagents on heat-inactivated samples","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Science for Life Laboratory; Vetenskapsrådet; Natural Science Foundation of Liaoning Province; Ragnar Söderbergs stiftelse; Knut och Alice Wallenbergs Stiftelse; Karolinska Institutet; Ministry of Science and Technology of the People's Republic of China; Swedish Foundation for International Cooperation in Research and Higher Education","keywords":"Limiting; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Reagent; Detection limit; Computer science; Virology; Computational biology; Biology; Chemistry; Medicine; Chromatography; Infectious disease (medical specialty); Pathology; 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":[0.001193396,0.0009942666,0.000532828,0.0004415271,0.0002557383,0.0006679046,0.0004646022,0.0006996695,0.001813494],"category_scores_gemma":[0.001188381,0.0004313701,0.0004938721,0.000431743,0.0004292254,0.0003608779,0.0003508171,0.0006547783,0.001191215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003819411,"about_ca_system_score_gemma":0.0003507737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006359987,"about_ca_topic_score_gemma":0.001722634,"domain_scores_codex":[0.9984541,0.000494226,0.0001238231,0.0003093194,0.000500985,0.0001175964],"domain_scores_gemma":[0.9993899,0.00022819,0.0001311278,0.00007434536,0.0001540313,0.00002230897],"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":[0.00003140133,0.00001515664,0.0001331044,0.00007254582,0.000005335216,0.00001468916,0.00001917587,0.000143075,0.9984018,0.00002581336,0.00003692969,0.001100886],"study_design_scores_gemma":[0.000001788537,0.00005961618,0.0003151152,0.000004616023,0.000005951895,0.00002727049,0.000008678128,0.0004566504,0.9985976,0.000008910635,0.0005100864,0.000003737602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8581999,0.002293945,0.1327301,0.00020985,0.0001074448,0.0004139921,0.001042134,0.000926496,0.004076106],"genre_scores_gemma":[0.7957139,0.003610925,0.1883764,0.0002393516,0.00004587866,0.0006000011,0.002318357,0.0003390106,0.008756058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001813494,"threshold_uncertainty_score":0.006311297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04211647370191898,"score_gpt":0.2702477079486084,"score_spread":0.2281312342466894,"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."}}