{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004578845,0.001020052,0.001632819,0.002699062,0.0005037431,0.001327117,0.0007860422,0.0007493776,0.002410661],"category_scores_gemma":[0.004842208,0.0005797719,0.01008117,0.005381402,0.0004553316,0.0008449918,0.001968161,0.001097675,0.0002070286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006883207,"about_ca_system_score_gemma":0.001115452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02130595,"about_ca_topic_score_gemma":0.01608966,"domain_scores_codex":[0.9977716,0.0009423225,0.0002851209,0.0005892108,0.0001798961,0.000231976],"domain_scores_gemma":[0.9962185,0.001261376,0.001073884,0.0007923669,0.0003932115,0.0002606437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006391128,0.00003367326,0.9667303,0.0009683398,0.02248829,0.0001402067,0.0001300128,0.001525256,0.0002660487,0.000203209,0.001009066,0.005866521],"study_design_scores_gemma":[0.00009621162,0.0001723451,0.974561,0.0003231138,0.01735287,0.0002037054,0.0002807199,0.004491798,0.0001443214,0.0003310735,0.002008493,0.00003436307],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9385498,0.04255943,0.005545524,0.0009728205,0.0001714637,0.0001218674,0.01103212,0.0001033855,0.0009434841],"genre_scores_gemma":[0.9914541,0.003131626,0.001538868,0.0001253646,0.00005535307,0.00007916177,0.003420395,0.00002780679,0.0001673876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02130595,"threshold_uncertainty_score":0.04236388,"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."}}