{"id":"W3080231310","doi":"10.1101/2020.08.22.20179507","title":"Detection of SARS-CoV-2 using non-commercial RT-LAMP reagents and raw samples","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Science for Life Laboratory; Vetenskapsrådet; Karolinska Institutet; 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); Benchmark (surveying); Computer science; 2019-20 coronavirus outbreak; Computational biology; Virology; Biology; Medicine; Infectious disease (medical specialty); Disease; Engineering","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":[0.0001098738,0.0002220058,0.0003809748,0.0001068519,0.00004403896,0.00003433248,0.0001013645,0.0002850645,0.000005362185],"category_scores_gemma":[0.00005133711,0.0002291558,0.0001195277,0.0001371535,0.00005087375,0.00003944668,0.0001437816,0.0004427516,0.000005688852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004991649,"about_ca_system_score_gemma":0.00001203276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002146004,"about_ca_topic_score_gemma":0.00007917653,"domain_scores_codex":[0.9990447,0.00003099458,0.0003235561,0.0002818419,0.0001505606,0.0001684112],"domain_scores_gemma":[0.9996009,0.00002780961,0.00007595873,0.0002075546,0.00003535865,0.00005244849],"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":[0.00001534676,0.000009482865,0.001019033,0.0004444863,0.00007513617,0.000004226009,0.00008157745,0.001019814,0.9903066,0.00000550112,0.00002888545,0.006989867],"study_design_scores_gemma":[0.0001631855,0.00002622207,0.01902313,0.0001135499,0.00009492796,0.000004516195,0.000009396302,0.2079009,0.7715237,0.0003889072,0.0005125235,0.0002390653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9531316,0.0001145694,0.0455756,0.00004248094,0.0006972328,0.000140955,0.00001890228,0.00009492173,0.0001837489],"genre_scores_gemma":[0.99884,0.0001435684,0.0006439025,0.00003162435,0.0002835946,0.000004166991,0.000006368758,0.00004295899,0.000003819727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.218783,"threshold_uncertainty_score":0.9344703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0735468884396063,"score_gpt":0.2833680247579946,"score_spread":0.2098211363183883,"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."}}