{"id":"W3046855964","doi":"10.1016/s1473-3099(20)30631-9","title":"SeroTracker: a global SARS-CoV-2 seroprevalence dashboard","year":2020,"lang":"en","type":"letter","venue":"The Lancet Infectious Diseases","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":239,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; McGill University; University of Toronto; Public Health Ontario; Institute of Infection and Immunity; University of Waterloo","funders":"Canadian Institutes of Health Research; Public Health Agency; Public Health Agency of Canada; McGill University; University of Calgary","keywords":"Seroprevalence; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Dashboard; 2019-20 coronavirus outbreak; Virology; Medicine; Computer science; Data science; Internal medicine; Antibody; Immunology; Outbreak; Infectious disease (medical specialty); Serology","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":[0.007531268,0.0009053546,0.001090304,0.0008881493,0.004339773,0.005529265,0.001807811,0.0448541,0.02037197],"category_scores_gemma":[0.02533065,0.0007819053,0.001356826,0.0006824502,0.002232255,0.003981646,0.003565047,0.03022724,0.01401324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003867109,"about_ca_system_score_gemma":0.009425078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01263567,"about_ca_topic_score_gemma":0.0209525,"domain_scores_codex":[0.9950906,0.001320079,0.0005646788,0.000407629,0.001579914,0.001037115],"domain_scores_gemma":[0.9770932,0.009472045,0.001283683,0.000647372,0.003138278,0.008365383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005606176,0.00005869459,0.0008213078,0.00002185383,0.00000782917,0.0004351462,0.000067619,0.00003473903,0.0001657974,0.0008059362,0.9892929,0.008232168],"study_design_scores_gemma":[0.0003425155,0.000256499,0.004908162,0.0003409507,0.00004627298,0.0004996099,0.0006567802,0.0006056194,0.0003770442,0.005482959,0.9864091,0.00007457047],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.0009718548,0.0003540006,0.000220024,0.9703921,0.02113072,0.00004841257,0.0004161218,0.0001295842,0.006337208],"genre_scores_gemma":[0.004798319,0.0003513897,0.0005690388,0.9612793,0.01559344,0.0001095398,0.0002750721,0.00005921015,0.01696464],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0448541,"threshold_uncertainty_score":0.06815106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.059850881668442,"score_gpt":0.3575416922187829,"score_spread":0.2976908105503409,"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."}}