{"id":"W3128925252","doi":"10.14207/ejsd.2021.v10n1p636","title":"Socio-Economic Risk Assessment and Peril Analysis in the Context of the COVID-19 Pandemic and Emergencies","year":2021,"lang":"en","type":"article","venue":"European Journal of Sustainable Development","topic":"Business and Economic Development","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Pandemic; Ranking (information retrieval); Government (linguistics); Coronavirus disease 2019 (COVID-19); Economic impact analysis; Business; Quarter (Canadian coin); Risk assessment; Economic cost; Public economics; Risk analysis (engineering); Quarantine; Economics; Actuarial science; Computer science; Geography; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004814742,0.0009489472,0.0005820226,0.004797113,0.0009099594,0.003144859,0.0009694098,0.001217313,0.0020332],"category_scores_gemma":[0.009412527,0.0002688394,0.001084574,0.002902814,0.001643055,0.002447312,0.003432922,0.001127501,0.0002615172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002388691,"about_ca_system_score_gemma":0.002466612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004266303,"about_ca_topic_score_gemma":0.003915346,"domain_scores_codex":[0.9957091,0.002222975,0.0002363331,0.000361811,0.00121946,0.0002502529],"domain_scores_gemma":[0.9949796,0.002597177,0.0009985864,0.0002809211,0.0009652646,0.0001785364],"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":[0.000324142,0.0004906849,0.203143,0.00137601,0.0005996657,0.003083188,0.007385164,0.2932048,0.003539271,0.2608809,0.005432047,0.2205411],"study_design_scores_gemma":[0.00002702541,0.0005256767,0.1381977,0.0006579412,0.0002764688,0.001255235,0.01649954,0.5290689,0.002951501,0.2830182,0.02727713,0.0002446464],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4283958,0.002691361,0.51394,0.003350956,0.0002196281,0.00110234,0.002200165,0.0002555313,0.0478443],"genre_scores_gemma":[0.9206305,0.001117305,0.07474427,0.00009294308,0.00005720992,0.0004582714,0.0005742082,0.00002505631,0.002300164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004814742,"threshold_uncertainty_score":0.0254631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880141150593133,"score_gpt":0.2390872636062089,"score_spread":0.2202858521002776,"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."}}