{"id":"W3018303981","doi":"10.1016/j.scitotenv.2020.138811","title":"A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain","year":2020,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":332,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada","funders":"Universidad Católica de Valencia San Vicente Màrtir","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; China; Demography; Population; Geography; Epidemiology; Environmental science; Climatology; Disease; Biology; Outbreak; Infectious disease (medical specialty); Medicine; Virology","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":[],"consensus_categories":[],"category_scores_codex":[0.003958506,0.0001319524,0.0003356196,0.00004829894,0.0002836597,0.0000103664,0.0006534394,0.00002749379,0.00001773265],"category_scores_gemma":[0.006510302,0.00005342738,0.0002346826,0.0007357077,0.0008364837,0.00006174325,0.0003764268,0.0001493731,0.000002464768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003077972,"about_ca_system_score_gemma":0.00002989033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002252096,"about_ca_topic_score_gemma":0.00001161798,"domain_scores_codex":[0.9982981,0.0003871267,0.0003185352,0.0002739894,0.0005060624,0.0002161701],"domain_scores_gemma":[0.9963328,0.002901016,0.0002405953,0.0004585335,0.000006991115,0.00006002047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007250239,0.000147236,0.05087905,0.0002168976,0.000267607,9.323419e-7,0.008262339,0.9082506,0.02241486,0.008230552,0.0001980452,0.0004068478],"study_design_scores_gemma":[0.001522161,0.002790552,0.8894141,0.00009548119,0.0007810777,0.000001002293,0.001327875,0.05145613,0.02548285,0.02657837,0.0001453205,0.000405125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845952,0.00004380036,0.0008377982,0.01361171,0.00003709696,0.0008315449,0.00001319462,0.000009704442,0.00001995178],"genre_scores_gemma":[0.9994617,0.000006425124,0.0002282119,0.0001231707,0.00002503209,0.0001185371,4.015814e-7,0.000004708236,0.00003185183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8567945,"threshold_uncertainty_score":0.7793911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1221623570439436,"score_gpt":0.3344768740429363,"score_spread":0.2123145169989927,"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."}}