{"id":"W4404228601","doi":"10.1097/ee9.0000000000000338","title":"Meteorological factors, population immunity, and COVID-19 incidence: A global multi-city analysis","year":2024,"lang":"en","type":"article","venue":"Environmental Epidemiology","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Health Canada","funders":"Norwegian Institute of Public Health; Centers for Disease Control and Prevention; University of Tsukuba; Helmholtz Zentrum München; Hokkaido University; Chinese Center for Disease Control and Prevention; Pusan National University; Korea University; Universidade de São Paulo; European Commission; Seoul National University; Monash University; Università degli Studi di Firenze; Queensland University of Technology; Harvard University; Emory University; Yale University","keywords":"Coronavirus disease 2019 (COVID-19); Incidence (geometry); Immunity; Population; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Geography; Medicine; Virology; Immunology; Environmental health; Immune system; Mathematics; Internal medicine; Infectious disease (medical specialty); Disease; Outbreak","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004257598,0.0004245621,0.001408802,0.0001312632,0.0003850145,0.00001922616,0.0002972515,0.0004673426,0.0007232009],"category_scores_gemma":[0.02602568,0.0003023998,0.0004517174,0.0004035439,0.0007678308,0.0001310523,0.0008952502,0.0004940941,0.00004063625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001080607,"about_ca_system_score_gemma":0.00001828923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003214927,"about_ca_topic_score_gemma":0.0005719221,"domain_scores_codex":[0.9947392,0.002473735,0.00102081,0.0009769828,0.0001811327,0.0006081802],"domain_scores_gemma":[0.9805665,0.01835258,0.0002455944,0.0004380028,0.000003983609,0.0003933455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002806249,0.0001171008,0.979953,0.00005288946,0.0006518631,0.00002521123,0.0001263948,0.0002807179,0.00003384991,0.01733681,0.0006965802,0.0006975161],"study_design_scores_gemma":[0.000183984,0.0001128361,0.8069778,0.000005784439,0.00054561,0.00001922302,0.0001065991,0.008716785,0.000002379903,0.1813767,0.001691955,0.0002603114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9230895,0.003623712,0.06933962,0.002904155,0.0001214714,0.0003403793,0.0002115511,0.0003194606,0.00005013711],"genre_scores_gemma":[0.9845538,0.0008942583,0.01095973,0.003195079,0.00005780253,0.00004206379,0.0002029584,0.00001514868,0.00007922283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1729752,"threshold_uncertainty_score":0.9999428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.314874129339784,"score_gpt":0.4752594997401723,"score_spread":0.1603853704003883,"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."}}