{"id":"W4362673293","doi":"10.1016/j.ijid.2023.04.002","title":"Mapping rabies distribution in China: a geospatial analysis of national surveillance data","year":2023,"lang":"en","type":"article","venue":"International Journal of Infectious Diseases","topic":"Rabies epidemiology and control","field":"Immunology and Microbiology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Rabies; Gross domestic product; Geography; China; Per capita; Mainland China; Incidence (geometry); Environmental health; Demography; Socioeconomics; Confidence interval; Veterinary medicine; Medicine; Population; Economic growth; Virology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008592386,0.00009722257,0.0004026317,0.00077104,0.00004747704,0.00001081925,0.0005008859,0.0000910141,0.0001677433],"category_scores_gemma":[0.002533456,0.00008570861,0.0001955323,0.00065407,0.0001300227,0.0002084359,0.0001302619,0.0001793135,0.00001967259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009553768,"about_ca_system_score_gemma":0.0001414958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001642081,"about_ca_topic_score_gemma":0.0002644279,"domain_scores_codex":[0.9986207,0.0002809255,0.0006601706,0.0001560736,0.0001336988,0.0001483576],"domain_scores_gemma":[0.9981339,0.0006658189,0.0005651821,0.0001426572,0.0004738079,0.00001861283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002552944,0.0001841513,0.9792829,0.000005728589,0.0036052,0.00002244143,0.0001311571,0.008716443,0.001100579,0.0006124204,0.00396467,0.002119015],"study_design_scores_gemma":[0.001191336,0.00004776425,0.9942914,0.00002853053,0.0001016446,0.00003604602,0.00008366231,0.001563724,0.00004475747,0.001155324,0.001380423,0.00007539389],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932691,0.0008263554,0.001945053,0.0007811206,0.001211845,0.0000542424,0.001762412,0.00002578071,0.0001241005],"genre_scores_gemma":[0.997201,0.0003114008,0.000003728441,0.00006413674,0.000106274,0.000004131602,0.002266702,0.000004239756,0.00003835579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01500849,"threshold_uncertainty_score":0.3495096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607044649731702,"score_gpt":0.2979803758365874,"score_spread":0.2819099293392703,"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."}}