{"id":"W2024923675","doi":"10.1016/j.sste.2014.06.004","title":"Bayesian tracking of emerging epidemics using ensemble optimal statistical interpolation","year":2014,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Climate variability and models","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mount Royal University","funders":"U.S. National Library of Medicine; Mount Royal University; National Institutes of Health; National Science Foundation","keywords":"Bayesian probability; Ensemble Kalman filter; Data assimilation; Tracking (education); Kalman filter; Computer science; Poisson distribution; Interpolation (computer graphics); Gaussian; Ensemble learning; Artificial intelligence; Statistics; Mathematics; Geography; Extended Kalman filter; Meteorology","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":[0.003785729,0.000486825,0.00137166,0.001755757,0.0007110494,0.001203914,0.001734438,0.001313227,0.001412634],"category_scores_gemma":[0.01405447,0.0009640469,0.001171141,0.001688969,0.0008303635,0.002130873,0.001662744,0.001827205,0.0002436263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001460042,"about_ca_system_score_gemma":0.0016103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03053387,"about_ca_topic_score_gemma":0.03107811,"domain_scores_codex":[0.9992583,0.0002866052,0.00004749032,0.0001716046,0.0001372755,0.00009872991],"domain_scores_gemma":[0.9913202,0.00623233,0.0007278986,0.0005591891,0.0009650657,0.0001953206],"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":[0.0000253033,0.00001595199,0.001736834,0.00001101126,0.00002370275,0.00001411761,0.00004064469,0.979066,0.0001696265,0.009571587,0.0002206104,0.009104608],"study_design_scores_gemma":[0.000001078868,0.000001912804,0.0000890575,0.000001805027,0.000001459312,0.000001894633,0.000001618024,0.9978002,0.00002874694,0.002027293,0.00004299985,0.000001937442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07443658,0.0002589092,0.9236467,0.0002127356,0.00004572906,0.00002435095,0.0001739845,0.00024255,0.0009583438],"genre_scores_gemma":[0.8465584,0.0004005734,0.1492227,0.0000811083,0.00006496757,0.00009411725,0.000748993,0.00007423762,0.002754971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03053387,"threshold_uncertainty_score":0.06071228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05055772028797293,"score_gpt":0.3164298932470581,"score_spread":0.2658721729590851,"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."}}