{"id":"W3035719209","doi":"10.1101/2020.06.11.20128777","title":"An international assessment of the COVID-19 pandemic using ensemble data assimilation","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Office of Naval Research; National Centre for Earth Observation; Natural Environment Research Council","keywords":"Data assimilation; Coronavirus disease 2019 (COVID-19); Computer science; Term (time); Computation; Pandemic; Ensemble forecasting; Econometrics; Mathematics; Artificial intelligence; Algorithm; Geography; Meteorology; Infectious disease (medical specialty)","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":[0.003662986,0.0005777965,0.0006473585,0.0007503161,0.000396068,0.00121835,0.0008757865,0.001122352,0.001235649],"category_scores_gemma":[0.006284102,0.0002744858,0.0008758415,0.0007058477,0.0003248794,0.002006472,0.001386408,0.0009441646,0.0001669846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008667462,"about_ca_system_score_gemma":0.0009473658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02807409,"about_ca_topic_score_gemma":0.01060068,"domain_scores_codex":[0.9993137,0.0004046795,0.00003300339,0.00009142597,0.0001044004,0.0000528743],"domain_scores_gemma":[0.9985656,0.0005722126,0.0001283343,0.0002818898,0.0003615625,0.00009051621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001371313,0.00006054142,0.01694129,0.00005040381,0.0001247648,0.00007860686,0.00005911879,0.9514945,0.000890122,0.00896656,0.001858227,0.01933877],"study_design_scores_gemma":[0.00001012486,0.00004345578,0.003597443,0.00001579577,0.00001285932,0.00001039395,0.00005147286,0.9915052,0.0003092108,0.003589507,0.0008406343,0.00001390651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8181341,0.001172347,0.1522446,0.004736466,0.0005111848,0.0001950841,0.005067445,0.0009193244,0.01701966],"genre_scores_gemma":[0.9703471,0.0002570495,0.02619425,0.0001047337,0.00005755358,0.0000480036,0.00213857,0.00007856565,0.0007741406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02807409,"threshold_uncertainty_score":0.05582136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7298420587714888,"score_gpt":0.5644989976560456,"score_spread":0.1653430611154432,"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."}}