{"id":"W4376641418","doi":"10.2196/44970","title":"Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Peking Union Medical College; Chinese Academy of Medical Sciences","keywords":"Epidemiology; Pandemic; Correlation; Coronavirus disease 2019 (COVID-19); Outbreak; Dominance (genetics); Demography; Medicine; Infectious disease (medical specialty); Environmental health; Biology; Disease; Virology; Internal medicine; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002279863,0.0003157303,0.0002903659,0.001098708,0.0002175892,0.0006349703,0.0003064078,0.0003168414,0.001289019],"category_scores_gemma":[0.005479005,0.0001850879,0.001023564,0.001102388,0.0002808433,0.0005122447,0.0006484536,0.0006588827,0.0002501872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000526642,"about_ca_system_score_gemma":0.0007751298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01242369,"about_ca_topic_score_gemma":0.010978,"domain_scores_codex":[0.9989884,0.0003167224,0.00009064777,0.0002988521,0.0001415317,0.0001638874],"domain_scores_gemma":[0.9952874,0.002280453,0.001258325,0.0002785111,0.0005085403,0.0003867149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007155157,0.000008015868,0.9958721,0.000009631314,0.0001589906,0.00005829825,0.00003855198,0.0014092,0.00008758167,0.00007070401,0.0002648574,0.001950416],"study_design_scores_gemma":[0.0000036488,0.0001166137,0.9815606,0.00001459945,0.0001108402,0.0001400288,0.0002186744,0.01682679,0.0001890617,0.0001976478,0.0006099651,0.00001154863],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958243,0.0004538962,0.001049286,0.0001995922,0.00003098872,0.00001234863,0.001734192,0.00003101516,0.0006644737],"genre_scores_gemma":[0.9987381,0.00005441319,0.0002012675,0.00001476682,0.00001225421,0.000007766523,0.0008522811,0.000003121037,0.0001159048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01242369,"threshold_uncertainty_score":0.02470279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.191342360033376,"score_gpt":0.4541226824747443,"score_spread":0.2627803224413683,"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."}}