{"id":"W3114724032","doi":"10.3934/fods.2021001","title":"An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation","year":2020,"lang":"en","type":"article","venue":"Foundations of Data Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Office of Naval Research; Agencia Nacional de Promoción Científica y Tecnológica; National Centre for Earth Observation; Natural Environment Research Council; Sight Research UK","keywords":"Coronavirus disease 2019 (COVID-19); Data assimilation; Term (time); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic; Computer science; Econometrics; Geography; Statistics; Mathematics; Meteorology; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002644212,0.00007993365,0.0001730707,0.00005631759,0.0003230351,0.00005013226,0.0033595,0.00002535532,0.00001395297],"category_scores_gemma":[0.02908917,0.00005654147,0.00001565491,0.000637495,0.0007321963,0.003193004,0.002170715,0.00011061,0.000002229774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005107856,"about_ca_system_score_gemma":0.0002672382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000117398,"about_ca_topic_score_gemma":0.00008122005,"domain_scores_codex":[0.9983726,0.0001121266,0.0004698181,0.0004517998,0.0004580749,0.0001356002],"domain_scores_gemma":[0.995854,0.00204078,0.0004926795,0.001296351,0.0002867413,0.00002948248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001045184,0.0006085925,0.3979377,0.0002385811,0.0002945007,0.000002234535,0.01156965,0.00234033,0.4912282,0.0724192,0.006198545,0.01705793],"study_design_scores_gemma":[0.0001885111,0.00006803047,0.02837665,0.000057977,0.00007118484,0.000002423059,0.0009411126,0.9512303,0.002449276,0.01528448,0.001209003,0.0001210248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5569313,0.00001723725,0.4366418,0.003336838,0.0001662265,0.0003300396,0.00164019,0.00006109109,0.0008753375],"genre_scores_gemma":[0.9287067,0.00001944416,0.0705565,0.000377061,0.00007211359,0.00000212612,0.0002616483,0.000003978234,4.359737e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.94889,"threshold_uncertainty_score":0.9790892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8207606729586456,"score_gpt":0.57113521935207,"score_spread":0.2496254536065756,"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."}}