{"id":"W2039501868","doi":"10.1007/s11269-010-9610-3","title":"An Integrated Simulation-Assessment Approach for Evaluating Health Risks of Groundwater Contamination Under Multiple Uncertainties","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Latin hypercube sampling; Fuzzy logic; Risk assessment; Health risk assessment; Environmental science; Stochastic simulation; Computer science; Sampling (signal processing); Uncertainty analysis; Health risk; Hydrogeology; Monte Carlo method; Risk analysis (engineering); Reliability engineering; Data mining; Engineering; Statistics; Mathematics; Simulation; Environmental health; Business; Artificial intelligence","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":[0.001180049,0.0002040644,0.0002409555,0.0001036904,0.0003578762,0.0001003217,0.0002709633,0.00005188011,0.0001922688],"category_scores_gemma":[0.000007786083,0.0001418254,0.0000709272,0.00010834,0.0001439926,0.0002684301,0.0001840058,0.0001155413,0.00001215737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001702981,"about_ca_system_score_gemma":0.000003895189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001706478,"about_ca_topic_score_gemma":0.0004888605,"domain_scores_codex":[0.9981449,0.0001401321,0.0004540002,0.0004326162,0.0004749674,0.0003533963],"domain_scores_gemma":[0.99932,0.0000575056,0.0001669109,0.0003293261,0.00005867421,0.00006764068],"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":[0.0001507213,0.001137518,0.1229044,0.0003216272,0.0002749298,9.773852e-7,0.01954767,0.5319635,0.01235263,0.0007439032,0.0002226743,0.3103795],"study_design_scores_gemma":[0.001955094,0.0006894042,0.3126775,0.00001976245,0.00008053553,6.768485e-7,0.009625728,0.6418975,0.001899468,0.0003655191,0.03039957,0.0003891946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6010777,0.000002993267,0.397382,0.0001241414,0.00008473411,0.0007721814,0.000004895018,0.00003960799,0.0005117227],"genre_scores_gemma":[0.9738589,0.000001715463,0.02338165,0.0001774112,0.00002820563,0.0002930264,0.0002177809,0.00002232335,0.002018966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3740003,"threshold_uncertainty_score":0.5783471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05801378167307983,"score_gpt":0.3482566656989557,"score_spread":0.2902428840258758,"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."}}