{"id":"W4391400384","doi":"10.22541/au.170668258.81174842/v1","title":"Seroprevalence and COVID-19 deaths in Indian Cities","year":2024,"lang":"en","type":"preprint","venue":"","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Seroprevalence; Coronavirus disease 2019 (COVID-19); Serology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Confidence interval; Population; Demography; Epidemiology; 2019-20 coronavirus outbreak; Geography; Virology; Medicine; Statistics; Environmental health; Mathematics; Immunology; Outbreak; Internal medicine; Antibody; Infectious disease (medical specialty); Sociology","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":[0.001185801,0.0002364487,0.0001814603,0.001726132,0.0004630617,0.001225357,0.000504686,0.0002508398,0.001145527],"category_scores_gemma":[0.005140279,0.0002076537,0.0003677438,0.001904271,0.0008800196,0.0004933482,0.0007853927,0.000672555,0.0001879237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022297,"about_ca_system_score_gemma":0.000441436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02473035,"about_ca_topic_score_gemma":0.01965139,"domain_scores_codex":[0.999416,0.0002244249,0.00003687942,0.0001141569,0.00008372344,0.0001247459],"domain_scores_gemma":[0.99743,0.0007939469,0.0007680369,0.0004324155,0.000415086,0.0001604861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001262607,0.00002942532,0.9886606,0.00001520188,0.00002690323,0.0001703765,0.0007235322,0.004270606,0.0006341513,0.0009345392,0.0002920264,0.004116437],"study_design_scores_gemma":[0.000007274085,0.0000352995,0.9841458,0.00001196096,0.00001658986,0.0002665122,0.00202216,0.01104057,0.001061938,0.0008475387,0.0005209562,0.00002344981],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983674,0.00002549671,0.00040751,0.00005440777,0.000002224744,0.000004567797,0.0001851828,0.00002079148,0.0009325261],"genre_scores_gemma":[0.9993877,0.00002473787,0.0002114995,0.000008315671,0.000001536514,0.000002355747,0.0002113263,0.000003470873,0.0001490454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02473035,"threshold_uncertainty_score":0.04917282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3377109717788575,"score_gpt":0.4705097861351731,"score_spread":0.1327988143563156,"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."}}