{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002234468,0.0002337417,0.0007443677,0.00003475849,0.0002237567,0.000008620011,0.0001222737,0.0002532891,0.0002536639],"category_scores_gemma":[0.002406848,0.0001109159,0.0001057442,0.000102562,0.0003268582,0.00009973492,0.0001719062,0.0002235477,0.000008564651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001060415,"about_ca_system_score_gemma":0.00000623369,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01219072,"about_ca_topic_score_gemma":0.003194363,"domain_scores_codex":[0.9969881,0.001215617,0.000778676,0.0005170192,0.00009402295,0.0004065644],"domain_scores_gemma":[0.9974138,0.001875212,0.0003785472,0.00006238406,0.00006803008,0.0002020124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001413581,0.00002469785,0.8678271,0.00003012139,0.00002020866,5.733446e-7,0.00002610265,0.00002263459,0.00113233,0.003402438,0.0002913955,0.127081],"study_design_scores_gemma":[0.0003129695,0.001025269,0.8908981,0.00002095247,0.00004005491,0.000003591561,0.00006585301,0.07288186,0.00002339097,0.008947845,0.02560091,0.000179206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905735,0.0003943655,0.004506199,0.003266514,0.0001693597,0.000236913,0.00004282064,0.00008318031,0.0007271998],"genre_scores_gemma":[0.9972328,0.0004710682,0.0005097242,0.0006770548,0.0004948847,0.00001796175,0.0004913567,0.000002599989,0.0001025735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1269018,"threshold_uncertainty_score":0.9943872,"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."}}