{"id":"W2058343295","doi":"10.1016/j.jenvrad.2006.03.002","title":"Fuzzy rule-based modelling for human health risk from naturally occurring radioactive materials in produced water","year":2006,"lang":"en","type":"article","venue":"Journal of Environmental Radioactivity","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Environmental science; Human health; Dilution; Produced water; Fuzzy logic; Fuzzy rule; Seawater; Pollutant; Fuzzy set; Environmental engineering; Computer science; Ecology; Biology; Environmental health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001495641,0.00067725,0.001065797,0.0008170027,0.0005411255,0.001828088,0.001650391,0.001986359,0.002216809],"category_scores_gemma":[0.003325135,0.0005030137,0.001386427,0.0007983564,0.000764158,0.001190499,0.0004646347,0.0007254664,0.0002926669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001653483,"about_ca_system_score_gemma":0.00106996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02459091,"about_ca_topic_score_gemma":0.01161871,"domain_scores_codex":[0.9993607,0.0002431632,0.00003972074,0.00009945326,0.0001746778,0.00008231278],"domain_scores_gemma":[0.9982131,0.001352323,0.0001238365,0.00004746693,0.0002200707,0.00004324977],"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":[0.0000402915,0.00001803526,0.0002263653,0.00001966224,0.0000177525,0.00008560621,0.00002778334,0.9954922,0.0004212187,0.001464226,0.00005622438,0.002130554],"study_design_scores_gemma":[0.00000462285,0.00001377384,0.00008234409,0.000002834564,0.000008809769,0.000009693767,0.000006734557,0.9984963,0.0001764012,0.001144233,0.00004975519,0.000004574812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3099414,0.0005496743,0.6810556,0.0003422422,0.00008128027,0.0001312845,0.0003988754,0.0002558862,0.007243793],"genre_scores_gemma":[0.970946,0.0001981799,0.02594916,0.0000280571,0.00001100406,0.00008079956,0.0001241731,0.00001624118,0.002646407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02459091,"threshold_uncertainty_score":0.04889554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07110085100246108,"score_gpt":0.3585448419880275,"score_spread":0.2874439909855664,"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."}}