{"id":"W3139332277","doi":"","title":"How do People Respond to Small Probability Events with Large, Negative Consequences?","year":2020,"lang":"en","type":"article","venue":"Repositório Institucional da Universidade Católica Portuguesa (Universidade Católica Portuguesa)","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Demographic economics; Dependency (UML); Natural experiment; Outbreak; Case fatality rate; Set (abstract data type); Economics; Actuarial science; Business; Econometrics; Demography; Statistics; Medicine; Sociology; Computer science; Disease; Mathematics","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":[0.002264278,0.0002314334,0.0004048412,0.0002856838,0.000341242,0.001180917,0.0003396335,0.00137814,0.004208046],"category_scores_gemma":[0.01477957,0.0002150138,0.0004227844,0.000269094,0.0009198139,0.001110936,0.0005933294,0.0008950176,0.0005581258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003404245,"about_ca_system_score_gemma":0.0002427512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467368,"about_ca_topic_score_gemma":0.0009807321,"domain_scores_codex":[0.9991809,0.0005017634,0.00002490366,0.0001233701,0.00006030651,0.0001088538],"domain_scores_gemma":[0.9939561,0.003752115,0.00138628,0.0004149398,0.0001459247,0.0003444827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004136218,0.004124057,0.7245837,0.0006550535,0.001063126,0.001855835,0.007889584,0.05654804,0.01924156,0.04580899,0.006368702,0.1277252],"study_design_scores_gemma":[0.001183058,0.007227403,0.5629111,0.0001464816,0.0005048003,0.001395196,0.0160757,0.1513951,0.004788626,0.2413211,0.0127635,0.000287957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901983,0.0001565958,0.003650415,0.002120086,0.00004180891,0.00005346702,0.0001869681,0.00001999861,0.003572387],"genre_scores_gemma":[0.998127,0.0001012911,0.0007186239,0.000380987,0.00001796415,0.00003330374,0.00004603175,0.000003013702,0.0005719019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004208046,"threshold_uncertainty_score":0.01407737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04947166385162,"score_gpt":0.2321274482045285,"score_spread":0.1826557843529085,"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."}}