{"id":"W3118135320","doi":"10.1080/10807039.2020.1855576","title":"Modeling exposure risk and prevention of mercury in drinking water for artisanal-small scale gold mining communities","year":2020,"lang":"en","type":"article","venue":"Human and Ecological Risk Assessment An International Journal","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hazard quotient; Risk assessment; Environmental health; Environmental science; Gold mining; Exposure assessment; Health risk assessment; Environmental resource management; Health risk; Environmental planning; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005894392,0.00009087549,0.0001585316,0.00004357433,0.0002354909,0.00009590336,0.0001328116,0.00004316803,0.0003478931],"category_scores_gemma":[0.00002855716,0.00006625518,0.00004634218,0.00002291385,0.00009087135,0.0003321517,0.0001554707,0.000208891,0.00000102044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004656073,"about_ca_system_score_gemma":0.000004829717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008439577,"about_ca_topic_score_gemma":0.0008970963,"domain_scores_codex":[0.999094,0.0001396721,0.0003320542,0.0001180826,0.0001736481,0.0001425064],"domain_scores_gemma":[0.9996448,0.00006402391,0.0001357899,0.00003772821,0.00002725676,0.00009039064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003944625,0.00008372425,0.979274,0.000004617368,0.00003838912,0.000002291248,0.005066505,0.005270371,0.001824767,0.0001033497,0.00002855681,0.008264019],"study_design_scores_gemma":[0.001436672,0.0009048997,0.874541,0.00004705078,0.00005432181,0.00001313365,0.01019065,0.1031091,0.0001473468,0.009158791,0.0002197112,0.0001772957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938225,0.0000264527,0.005405772,0.0002319621,0.00005602766,0.0001081408,0.000008833834,0.00000755476,0.0003327714],"genre_scores_gemma":[0.9938664,0.0004882925,0.005405493,0.0001106581,0.00007448796,0.00001068027,0.00001662718,0.000004827013,0.00002257517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1047329,"threshold_uncertainty_score":0.3809186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07037373844758733,"score_gpt":0.3323357507607594,"score_spread":0.2619620123131721,"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."}}