{"id":"W3162156345","doi":"10.3390/jrfm14100495","title":"The COVID-19 Shock: A Bayesian Approach","year":2021,"lang":"en","type":"dissertation","venue":"Journal of risk and financial management","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Bayesian probability; Shock (circulatory); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Virology; Artificial intelligence; Medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.005185025,0.0006402132,0.001318046,0.002332592,0.0006663764,0.002667133,0.00170013,0.002160074,0.006136952],"category_scores_gemma":[0.02530824,0.0008659245,0.001151609,0.001569642,0.00129593,0.002871895,0.001731907,0.002967715,0.0005042324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001834179,"about_ca_system_score_gemma":0.00159113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01834016,"about_ca_topic_score_gemma":0.01347033,"domain_scores_codex":[0.9986161,0.000883805,0.00005111972,0.0001754316,0.0001442807,0.0001293476],"domain_scores_gemma":[0.9874802,0.01068446,0.0008265499,0.0002177676,0.0005291351,0.0002617466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000080839,0.00008811949,0.006342099,0.000124718,0.0001758596,0.0002577064,0.000247373,0.4527926,0.0002747089,0.4939442,0.004575459,0.04109621],"study_design_scores_gemma":[0.00002629852,0.00003647063,0.00159266,0.00005664877,0.00003944018,0.00005623962,0.0001066322,0.70355,0.0000732226,0.291511,0.002921584,0.00002971017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08257333,0.002824137,0.8866129,0.008827562,0.0002318945,0.0001338737,0.000817513,0.0001671819,0.01781165],"genre_scores_gemma":[0.8245576,0.006423451,0.1417139,0.001109195,0.0009983469,0.0003401611,0.001037535,0.0001096795,0.02370998],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01834016,"threshold_uncertainty_score":0.03646684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0776468468102021,"score_gpt":0.366220386548804,"score_spread":0.2885735397386019,"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."}}