{"id":"W2143875496","doi":"10.4081/gh.2013.61","title":"Schistosoma japonicum risk in Jiangsu province, People’s Republic of China: identification of a spatio-temporal risk pattern along the Yangtze River","year":2013,"lang":"en","type":"article","venue":"Geospatial health","topic":"Parasites and Host Interactions","field":"Immunology and Microbiology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; International Development Research Centre","keywords":"Schistosoma japonicum; China; Yangtze river; Geography; Schistosomiasis; Transmission (telecommunications); Veterinary medicine; Schistosoma; Distribution (mathematics); Spatial distribution; Biology; Ecology; Physical geography; Helminths; Zoology; Medicine; Remote sensing; Schistosoma mansoni","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005565712,0.0001927603,0.0001545773,0.001039292,0.0003567769,0.0002834887,0.0002248797,0.0001993364,0.0003524028],"category_scores_gemma":[0.0007524707,0.0001668969,0.0001942172,0.00115894,0.0003489183,0.0001973976,0.000323184,0.0001364724,0.00003096117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007333908,"about_ca_system_score_gemma":0.0008793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07326575,"about_ca_topic_score_gemma":0.1265022,"domain_scores_codex":[0.9997271,0.00008580591,0.00002605189,0.00005411463,0.00005369822,0.00005314317],"domain_scores_gemma":[0.9992647,0.0001189997,0.0003232979,0.00004437356,0.0001342661,0.0001143843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004967604,0.00001084078,0.9965904,0.00001111322,0.0000265528,0.00009906195,0.0002323415,0.0001831355,0.0009306932,0.00003168222,0.00004361489,0.001790862],"study_design_scores_gemma":[0.000001761498,0.00002033491,0.9994049,0.000001538626,0.000005983164,0.00003547002,0.0001647413,0.0002665816,0.00004469567,0.00001000579,0.00004226321,0.000001667826],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999719,0.00004555933,0.00005225989,0.00002186986,6.742145e-7,0.000003298334,0.00006380809,0.000001686532,0.00009171302],"genre_scores_gemma":[0.9997436,0.00002937167,0.00006821703,0.000004065777,0.000001208951,0.000004146315,0.00008118498,3.031252e-7,0.00006784165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07326575,"threshold_uncertainty_score":0.1456786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00706448317435704,"score_gpt":0.2570233927794458,"score_spread":0.2499589096050888,"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."}}