{"id":"W2163193677","doi":"10.1186/1756-3305-6-138","title":"Spatio-temporal analysis to identify determinants of Oncomelania hupensis infection with Schistosoma japonicum in Jiangsu province, China","year":2013,"lang":"en","type":"article","venue":"Parasites & Vectors","topic":"Parasites and Host Interactions","field":"Immunology and Microbiology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; International Development Research Centre","keywords":"Oncomelania hupensis; Snail; Biology; Schistosoma japonicum; Oncomelania; Livestock; SCHISTOSOMIASIS JAPONICA; Spatial distribution; China; Schistosomiasis; Ecology; Schistosoma; Spatial analysis; Veterinary medicine; Geography; Zoology; Helminths; 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":[0.0007258118,0.0001926135,0.0001773383,0.001300757,0.0003278374,0.0002927741,0.000296236,0.0001698,0.0005276157],"category_scores_gemma":[0.001174725,0.0001506157,0.0003286422,0.001318225,0.0002493915,0.0001850979,0.0002927869,0.0001370043,0.00005139382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003784,"about_ca_system_score_gemma":0.001117435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1093591,"about_ca_topic_score_gemma":0.1384411,"domain_scores_codex":[0.9996933,0.00007930413,0.00003485219,0.00006700455,0.00005824849,0.00006739805],"domain_scores_gemma":[0.9988998,0.0002066161,0.0004346978,0.00006434029,0.0002353867,0.0001589912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003842533,0.00001458995,0.9971034,0.00001731623,0.00003677125,0.0001092785,0.000125487,0.0003261201,0.0004748598,0.0000264523,0.00007574292,0.001651504],"study_design_scores_gemma":[9.894189e-7,0.00001219164,0.9987496,0.000002957041,0.000009212865,0.00002721987,0.0001337109,0.0009693112,0.00003091872,0.000007226538,0.00005538399,0.000001300455],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995215,0.00006315612,0.00009614163,0.00002639265,0.000001740429,0.000005489886,0.0001556014,0.000003058701,0.0001269215],"genre_scores_gemma":[0.9996188,0.00002722829,0.00008687354,0.000004577663,0.000001552298,0.000005169029,0.0001832558,4.700159e-7,0.00007214581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1093591,"threshold_uncertainty_score":0.2174451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008815155138791723,"score_gpt":0.2954244678470349,"score_spread":0.2866093127082432,"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."}}