{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004361161,0.0004825859,0.0005183329,0.001052999,0.0006896605,0.001795598,0.001366197,0.001142779,0.005881501],"category_scores_gemma":[0.01227951,0.0003773351,0.0009236034,0.0008550016,0.0009367347,0.001567732,0.001859951,0.001682261,0.0003519202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002367507,"about_ca_system_score_gemma":0.002705522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03201418,"about_ca_topic_score_gemma":0.01924108,"domain_scores_codex":[0.9980855,0.0009777842,0.00006916569,0.0002970174,0.0003877141,0.0001828278],"domain_scores_gemma":[0.9970295,0.001982961,0.00037925,0.0001736548,0.0003364839,0.00009809345],"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":[0.00003439129,0.0000292889,0.003758182,0.00008864502,0.00006050839,0.000125121,0.0003135792,0.2983176,0.0003520839,0.6702341,0.003509616,0.02317693],"study_design_scores_gemma":[0.00001466121,0.00004808618,0.002595962,0.0001008646,0.00004117692,0.00008336097,0.0002751921,0.5792519,0.0003183656,0.389298,0.02793099,0.00004147439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05381479,0.002078244,0.8927503,0.01084525,0.0004422295,0.0001666573,0.002271304,0.0003673343,0.03726395],"genre_scores_gemma":[0.8519375,0.003030392,0.1284814,0.0004649796,0.0002763045,0.0003034521,0.001362849,0.0000878281,0.01405529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03201418,"threshold_uncertainty_score":0.06365567,"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."}}