{"id":"W4286001834","doi":"10.1038/s41598-022-16799-8","title":"Dynamic causal modelling of COVID-19 and its mitigations","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Australian Research Council; Wellcome Trust; Canadian Institute for Advanced Research","keywords":"Computer science; Bayes' theorem; Model selection; Bayesian probability; Causal model; Coronavirus disease 2019 (COVID-19); Econometrics; Machine learning; Bayes factor; Bayesian inference; Artificial intelligence; Data mining; Statistics; Mathematics; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003551109,0.0001023891,0.0002735524,0.0001160022,0.0009239652,0.00002764285,0.0001078485,0.00002903672,0.0002406456],"category_scores_gemma":[0.004855652,0.00009062203,0.0000740782,0.000415688,0.0002242677,0.00005590426,0.0003987671,0.0001307969,0.000001574697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001635672,"about_ca_system_score_gemma":0.0001868284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006099433,"about_ca_topic_score_gemma":0.00003734111,"domain_scores_codex":[0.9980441,0.0001450006,0.0006072302,0.0005467706,0.0004400631,0.0002168321],"domain_scores_gemma":[0.9979516,0.0009909272,0.0004156353,0.0004189583,0.00009013061,0.0001327556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006285244,0.001228019,0.03502524,0.002745308,0.0004039673,0.001860564,0.01879317,0.5988048,0.02582097,0.1712189,0.1431981,0.000838003],"study_design_scores_gemma":[0.0000976052,0.00003262133,0.0001465219,0.00001063715,0.00004189805,0.0001302844,0.0004904525,0.09277193,0.0002104437,0.8869187,0.01898945,0.0001594249],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698489,0.0007336091,0.02497312,0.002038669,0.001401508,0.0004552968,0.00002945728,0.0001105195,0.0004088659],"genre_scores_gemma":[0.994508,0.000009214375,0.003363618,0.0001280424,0.000007539553,0.00006644644,0.00001641818,0.00000889382,0.001891832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7156998,"threshold_uncertainty_score":0.7106487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2550194651921048,"score_gpt":0.4097808730228993,"score_spread":0.1547614078307945,"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."}}