{"id":"W4388570126","doi":"10.3390/dynamics3040041","title":"Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes","year":2023,"lang":"en","type":"article","venue":"Dynamics","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Agencia Estatal de Investigación; Basque Center for Applied Mathematics; Javna Agencija za Raziskovalno Dejavnost RS; Institute of Physics Belgrade","keywords":"Pandemic; Case fatality rate; Geography; Econometrics; Preparedness; Complex network; Cluster analysis; Population; Computer science; Statistics; Economic geography; Data science; Demography; Coronavirus disease 2019 (COVID-19); Medicine; Infectious disease (medical specialty); Mathematics; Economics; Sociology; Disease","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002391806,0.00009304408,0.0003239166,0.0001308563,0.0001728094,0.00005001308,0.0001479942,0.00006353454,0.00005699812],"category_scores_gemma":[0.00004291493,0.00007445702,0.000105346,0.001011961,0.0000974772,0.00006021815,0.0001219684,0.00005998769,0.00001191712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006069483,"about_ca_system_score_gemma":0.00000651783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002270825,"about_ca_topic_score_gemma":0.0006772049,"domain_scores_codex":[0.9991833,0.00001148085,0.0003911013,0.0002174704,0.00002663196,0.0001699927],"domain_scores_gemma":[0.9995596,0.00003432264,0.0002352449,0.0001223264,0.0000284598,0.00002006503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003257409,0.0000380073,0.8935311,0.0001568983,0.0004158381,0.000004806241,0.001180054,0.00193189,0.00001186536,0.09761836,0.000144744,0.004933876],"study_design_scores_gemma":[0.0004588568,0.00005855525,0.59663,0.00002474026,0.00005257716,0.00001462951,0.003107589,0.3842679,0.000003387155,0.01264854,0.002366304,0.0003669783],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952946,0.00042954,0.001117679,0.0004177795,0.0000613396,0.00003215533,0.0002962207,0.00002781481,0.002322876],"genre_scores_gemma":[0.9993292,0.0001222083,0.00004223679,0.00001157857,0.00005552217,0.000003679235,0.00005537554,0.000006048652,0.0003741609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.382336,"threshold_uncertainty_score":0.3036269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04409732118596954,"score_gpt":0.2476965058337367,"score_spread":0.2035991846477672,"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."}}