{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001554377,0.001011227,0.0004860357,0.0004567502,0.000257675,0.0008149722,0.0004996212,0.0007042473,0.002041444],"category_scores_gemma":[0.001154619,0.0003793625,0.0004059544,0.0005447639,0.0003676158,0.0003026397,0.0002912712,0.0005835807,0.001598418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000485741,"about_ca_system_score_gemma":0.0004232486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009584064,"about_ca_topic_score_gemma":0.001748818,"domain_scores_codex":[0.9982952,0.0005247229,0.0001336093,0.0002993402,0.0006202532,0.0001267821],"domain_scores_gemma":[0.999589,0.0001323558,0.00007262855,0.00005807531,0.0001299504,0.0000178467],"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":[0.00005950467,0.00003466903,0.0002231569,0.00009468017,0.000007699628,0.00002305835,0.00002257466,0.0002888101,0.997577,0.00005450653,0.0001206499,0.001493683],"study_design_scores_gemma":[0.000003176645,0.0000638711,0.0003684117,0.000005423429,0.000005319377,0.00002889549,0.00001151479,0.0005424939,0.997655,0.00001881735,0.001291556,0.000005450416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8518958,0.003028119,0.1327447,0.0003223191,0.0001596499,0.0007630629,0.004089148,0.001569688,0.00542749],"genre_scores_gemma":[0.7662033,0.002521312,0.2077378,0.0002459262,0.00004579695,0.0008476078,0.008992784,0.0005760383,0.01282949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002041444,"threshold_uncertainty_score":0.008220494,"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."}}