{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007326815,0.000387445,0.001129655,0.0006732353,0.0003733211,0.0003455101,0.0008087473,0.0001572412,0.0002639125],"category_scores_gemma":[0.000346762,0.0002731039,0.0003767633,0.0001750959,0.0001314741,0.001220107,0.0001258735,0.0005553989,0.00002198073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075686,"about_ca_system_score_gemma":0.00008266685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006049442,"about_ca_topic_score_gemma":0.00009232459,"domain_scores_codex":[0.9935608,0.001037178,0.002216793,0.0007696621,0.001789054,0.0006265354],"domain_scores_gemma":[0.995566,0.001525844,0.002050831,0.000601905,0.00005922081,0.000196236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001522182,0.001024566,0.03828606,0.00001215983,0.00007186877,0.00005922821,0.0008363891,0.0971581,0.8472672,0.00002569548,0.0002790569,0.01345751],"study_design_scores_gemma":[0.008890207,0.0006051676,0.2724088,0.00023506,0.00008366164,0.00006906788,0.0005639086,0.01771545,0.6471259,0.05023739,0.001133781,0.000931557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9571291,0.0002282791,0.04054638,0.0002288658,0.0007925402,0.0006037966,0.0004303561,0.00001272683,0.00002798353],"genre_scores_gemma":[0.9760142,0.00000934639,0.02319425,0.0001263444,0.0005510416,0.00001272543,0.00002138064,0.00004436102,0.00002635202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2341228,"threshold_uncertainty_score":0.9999721,"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."}}