{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003484119,0.0001401697,0.0002367927,0.0001638753,0.0001702071,0.00009732408,0.0000441806,0.0001032869,0.0000213927],"category_scores_gemma":[0.0001172766,0.0001380952,0.0001278516,0.0007549667,0.00007258811,0.0001097456,0.0000250526,0.0001293861,0.000007813676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000123449,"about_ca_system_score_gemma":0.00003781961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001361628,"about_ca_topic_score_gemma":0.000104944,"domain_scores_codex":[0.9985915,0.00003235212,0.0004091342,0.000416235,0.0003161836,0.0002346272],"domain_scores_gemma":[0.9992761,0.00002475687,0.0000727638,0.0004453125,0.0001327787,0.00004831276],"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":[0.000007313827,0.00004130599,0.0001287575,0.00003385602,0.0000265958,0.00008801428,0.00003250015,0.001655848,0.9935015,0.000001693665,0.0003733514,0.004109259],"study_design_scores_gemma":[0.00007208258,0.00001903911,0.001429502,0.00006201409,0.00002469037,0.00006831767,0.00001736037,0.0184929,0.9758009,0.0001418458,0.003733387,0.0001379071],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887441,0.00003183472,0.003535811,0.00001083945,0.005353268,0.00009256491,0.000004180552,0.00009539404,0.002131953],"genre_scores_gemma":[0.9994858,0.00000503498,0.0002322509,0.00001171601,0.00008779622,0.000002358083,0.00001452022,0.00002281175,0.0001377303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01770055,"threshold_uncertainty_score":0.5631358,"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."}}