{"id":"W4281739699","doi":"10.1016/j.ijdrr.2022.103078","title":"Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities","year":2022,"lang":"en","type":"article","venue":"International Journal of Disaster Risk Reduction","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Health Commission of Sichuan Province; Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China; Michigan State University; King Abdullah University of Science and Technology; Chengdu Federation of Social Science Association; Daqing Science and Technology Bureau","keywords":"Bayesian probability; Outlier; Geography; Public health; Socioeconomic status; Pandemic; Econometrics; Population; Coronavirus disease 2019 (COVID-19); Environmental health; Computer science; Medicine; Economics; Artificial intelligence","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":[0.003630532,0.0008784274,0.000787332,0.001130511,0.0004854642,0.001160209,0.001957991,0.0008459186,0.002494918],"category_scores_gemma":[0.008129729,0.0004932201,0.002441405,0.0009766667,0.0008276867,0.0009666484,0.001401576,0.001369768,0.0002724797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001626589,"about_ca_system_score_gemma":0.001866498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1867806,"about_ca_topic_score_gemma":0.07030745,"domain_scores_codex":[0.9987475,0.0004905058,0.00006470352,0.0004010876,0.00009151718,0.0002046902],"domain_scores_gemma":[0.9968279,0.001667408,0.0005471469,0.0002473628,0.0005018006,0.0002083472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003042482,0.0002497945,0.3583732,0.0001415423,0.0006459273,0.000396731,0.001004682,0.5952507,0.001023219,0.01453547,0.00381668,0.02425788],"study_design_scores_gemma":[0.00001510192,0.00003815834,0.02417036,0.00001651751,0.00009502753,0.00001482631,0.0001823872,0.9729118,0.00009475957,0.001895884,0.0005429726,0.00002219247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9427958,0.0005100313,0.05122412,0.001304726,0.000108832,0.0001133517,0.002020749,0.0001708844,0.001751493],"genre_scores_gemma":[0.9930396,0.0001962687,0.003472395,0.00008020463,0.0000362721,0.00008082166,0.001458605,0.00002456248,0.00161122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1867806,"threshold_uncertainty_score":0.3713869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1852777122655509,"score_gpt":0.4227827090243811,"score_spread":0.2375049967588302,"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."}}