{"id":"W1992683141","doi":"10.1016/j.sste.2014.08.003","title":"Supervised learning and prediction of spatial epidemics","year":2014,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Markov chain Monte Carlo; Inference; Computer science; Machine learning; Infectious disease (medical specialty); Artificial intelligence; Bayesian inference; Classifier (UML); Bayesian probability; Epidemic model; Markov chain; Data mining; Disease; Population; 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":[0.004682945,0.0008796058,0.00209227,0.001843746,0.0005385875,0.0012144,0.002744052,0.001974331,0.001309159],"category_scores_gemma":[0.01511057,0.0009219759,0.001394825,0.001051331,0.00112513,0.001990271,0.001421499,0.002242574,0.0003089292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001481814,"about_ca_system_score_gemma":0.001327324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01171753,"about_ca_topic_score_gemma":0.01085875,"domain_scores_codex":[0.9986008,0.0006139906,0.0001210203,0.0003921932,0.0001274285,0.000144629],"domain_scores_gemma":[0.9725375,0.02350909,0.00153516,0.0008834206,0.001094327,0.0004405675],"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":[0.0001718507,0.0002325832,0.007695768,0.00009174168,0.0001295271,0.00004951496,0.00005418322,0.9470518,0.0002524604,0.003465909,0.001677164,0.03912756],"study_design_scores_gemma":[0.000005690045,0.000007584184,0.0001522061,0.00000232813,0.000003335897,0.000002843288,0.000002352551,0.9979912,0.00003363307,0.001767708,0.00002978438,0.000001367914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3060972,0.002231193,0.6858501,0.001838391,0.0002074749,0.0001054153,0.001109515,0.001413669,0.001147047],"genre_scores_gemma":[0.9591575,0.0003881051,0.03644612,0.0001549491,0.0002551523,0.0001063607,0.001495077,0.00005408928,0.001942526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01171753,"threshold_uncertainty_score":0.02476609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03809469576115967,"score_gpt":0.2548219849320543,"score_spread":0.2167272891708946,"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."}}