{"id":"W4220763024","doi":"10.1016/j.idm.2022.03.001","title":"Seroprevalence and infection attack rate of COVID-19 in Indian cities","year":2022,"lang":"en","type":"article","venue":"Infectious Disease Modelling","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Seroprevalence; Serology; Case fatality rate; Coronavirus disease 2019 (COVID-19); Infection rate; Virology; Attack rate; Demography; Medicine; Veterinary medicine; Environmental health; Immunology; Antibody; Outbreak; Infectious disease (medical specialty); Internal medicine; Population; Disease; Surgery","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":[0.001093166,0.0001772921,0.000358198,0.0002159089,0.0003661293,0.0000162494,0.0000909209,0.00004296232,0.0001117533],"category_scores_gemma":[0.002146093,0.0001772729,0.00009233042,0.0003523463,0.0001309142,0.0001003238,0.00028306,0.0002681261,0.000002019783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004197356,"about_ca_system_score_gemma":0.0001386757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001639178,"about_ca_topic_score_gemma":0.0003111883,"domain_scores_codex":[0.9982392,0.0005233109,0.0004446178,0.0003598294,0.0001717732,0.0002612291],"domain_scores_gemma":[0.997451,0.001874937,0.000211298,0.000220576,0.00004211647,0.0002000884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006024251,0.0001242928,0.3078657,0.00065479,0.0000237324,0.0000216679,0.001538156,0.6866363,0.000007211207,0.002848634,0.0001267347,0.00009245156],"study_design_scores_gemma":[0.001161841,0.0002011544,0.01637425,0.00008998628,0.0001139929,0.00001094124,0.0004674869,0.4289341,0.00001265895,0.5508437,0.001303038,0.0004868255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9574828,0.0004380086,0.04092326,0.0003709867,0.00009056245,0.0003941765,0.0000444763,0.0001458589,0.0001098897],"genre_scores_gemma":[0.9982419,0.0005147702,0.0001976352,0.0007480374,0.0000302535,0.0001980937,0.000004722584,0.00001845387,0.00004615777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5479951,"threshold_uncertainty_score":0.7228979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2275506082593648,"score_gpt":0.4003066019133565,"score_spread":0.1727559936539917,"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."}}