{"id":"W2161518653","doi":"10.1016/j.jbi.2014.10.009","title":"Quantifying the determinants of outbreak detection performance through simulation and machine learning","year":2014,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McGill University Health Centre; Polytechnique Montréal","funders":"Canadian Institutes of Health Research; U.S. Public Health Service","keywords":"Computer science; Outbreak; Data mining; Machine learning; Probabilistic logic; Bayesian probability; Artificial intelligence; Range (aeronautics); Engineering","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":[0.001045437,0.00008892302,0.0003103254,0.0001018258,0.00008504283,0.00001684317,0.00009360641,0.00006528855,0.00001167035],"category_scores_gemma":[0.0009576163,0.00005168118,0.00006599193,0.0001712406,0.0001726569,0.0003546419,0.00005096643,0.0003066181,0.000003266213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002370701,"about_ca_system_score_gemma":0.00004317323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004246687,"about_ca_topic_score_gemma":0.000003134674,"domain_scores_codex":[0.9984219,0.0000555728,0.0008759961,0.00003704401,0.0004831806,0.0001262762],"domain_scores_gemma":[0.9984875,0.0002718228,0.0008270853,0.0001288551,0.0001797457,0.0001049334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005320608,0.0001368984,0.2902523,0.00154802,0.0001189008,0.000007304982,0.003398552,0.002298706,0.0009025612,0.0000129468,0.0000691786,0.7007226],"study_design_scores_gemma":[0.001338124,0.0008808378,0.09231079,0.0004175434,0.0000980382,0.0002797144,0.0003645,0.8837329,0.001108433,0.00001791055,0.01937624,0.00007498227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752873,0.0001100572,0.02412251,0.00009344206,0.0001566903,0.00008262276,0.000005027563,0.00001132228,0.000131059],"genre_scores_gemma":[0.9975442,0.0002171566,0.001946715,0.0001581688,0.000111511,4.570298e-7,0.000006349026,0.000006984261,0.000008449356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8814342,"threshold_uncertainty_score":0.2107497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04316760951373722,"score_gpt":0.3256251716509549,"score_spread":0.2824575621372177,"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."}}