{"id":"W3021716136","doi":"10.1101/2020.05.02.20087924","title":"A strategy for finding people infected with SARS-CoV-2: optimizing pooled testing at low prevalence","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"Ministry of Colleges and Universities; Global Affairs Canada; Carnegie Corporation of New York; International Development Research Centre; Government of Canada; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Group testing; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Test strategy; Isolation (microbiology); Sample (material); Coronavirus disease 2019 (COVID-19); Computer science; Replicate; Medicine; Statistics; Disease; Virology; Infectious disease (medical specialty); Biology; Mathematics; Bioinformatics; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006985229,0.001839118,0.002238217,0.001698218,0.000772349,0.001570176,0.001810623,0.001688947,0.002717664],"category_scores_gemma":[0.0121811,0.0008927985,0.001661535,0.001039045,0.001264355,0.001979805,0.00205265,0.001217421,0.001411397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000721447,"about_ca_system_score_gemma":0.001799464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001536844,"about_ca_topic_score_gemma":0.001784832,"domain_scores_codex":[0.9953085,0.002628801,0.0002028551,0.001037339,0.000533991,0.000288469],"domain_scores_gemma":[0.9960359,0.001664963,0.0006196141,0.0007160028,0.000654463,0.0003091134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004984807,0.002982341,0.03375206,0.001052639,0.0009972717,0.0006696391,0.0007812883,0.05092661,0.4240066,0.006874265,0.007484352,0.465488],"study_design_scores_gemma":[0.001856037,0.01985914,0.03958104,0.0003714985,0.00209313,0.003452974,0.001689904,0.419629,0.4235788,0.0617268,0.02553897,0.0006227079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1651096,0.001588656,0.8225241,0.002888523,0.0002385767,0.001859522,0.0006727662,0.002276707,0.002841625],"genre_scores_gemma":[0.319606,0.0005090251,0.6752478,0.001043026,0.00009989654,0.001245564,0.0007357558,0.0001185897,0.001394332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006985229,"threshold_uncertainty_score":0.03694189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010831857288206,"score_gpt":0.3289363054486616,"score_spread":0.227853119719841,"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."}}