{"id":"W2900925615","doi":"10.4081/gh.2018.665","title":"Bayesian zero-inflated spatio-temporal modelling of scrub typhus data in Korea, 2010-2014","year":2018,"lang":"en","type":"article","venue":"Geospatial health","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Scrub typhus; Outbreak; Bayesian probability; Covariate; Orientia tsutsugamushi; Geography; Statistics; Medicine; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006377141,0.0004543692,0.0006210636,0.001039048,0.0003281385,0.0009265503,0.001501477,0.0009894242,0.001449184],"category_scores_gemma":[0.0133033,0.0005384312,0.001315646,0.001428034,0.0005304605,0.001042098,0.0008430451,0.00112811,0.0003357473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00120462,"about_ca_system_score_gemma":0.0008647973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07721692,"about_ca_topic_score_gemma":0.05707762,"domain_scores_codex":[0.9981722,0.001074804,0.0001176349,0.0003601816,0.00009764227,0.0001774264],"domain_scores_gemma":[0.9929732,0.004319317,0.001190473,0.0005745043,0.0007234222,0.0002191795],"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":[0.0004238939,0.0002414358,0.4248727,0.0001261649,0.0005040233,0.0004853279,0.0005875964,0.537643,0.0008583986,0.01345788,0.003123245,0.01767631],"study_design_scores_gemma":[0.00002303345,0.00006148114,0.07648086,0.0000329434,0.0000738327,0.00005758553,0.0002115103,0.9179967,0.0002096219,0.003367565,0.00144875,0.00003610807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685649,0.0004240093,0.02508149,0.0006396427,0.00002978106,0.00004138778,0.004487354,0.00009407754,0.0006373532],"genre_scores_gemma":[0.9883754,0.0001579488,0.005012022,0.00003916468,0.00001190886,0.00003655695,0.005563939,0.00001793539,0.0007852037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07721692,"threshold_uncertainty_score":0.1535349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09654715935717338,"score_gpt":0.2773049509166248,"score_spread":0.1807577915594514,"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."}}