{"id":"W1995761515","doi":"10.1061/(asce)0733-9496(2009)135:6(547)","title":"Fuzzy-Logic Modeling of Risk Assessment for a Small Drinking-Water Supply System","year":2009,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Canada Research Chairs","keywords":"Fault tree analysis; Fuzzy logic; Risk analysis (engineering); Computer science; Fuzzy set; Set (abstract data type); Water supply; Reliability engineering; Sensitivity (control systems); Process (computing); Decision tree; Engineering; Data mining; Business; Artificial intelligence; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0007847052,0.000926669,0.0007465631,0.001036025,0.0009891414,0.00158215,0.001285912,0.001358563,0.004658195],"category_scores_gemma":[0.001723426,0.0004647822,0.0008603499,0.001065227,0.0009444093,0.001268965,0.0007747421,0.000963093,0.0002982805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002390077,"about_ca_system_score_gemma":0.002065063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04491804,"about_ca_topic_score_gemma":0.0338732,"domain_scores_codex":[0.9995958,0.0001594397,0.00001944981,0.0000497016,0.0001125929,0.00006302381],"domain_scores_gemma":[0.9992626,0.0004907401,0.00007715901,0.0000134116,0.0001128894,0.00004304993],"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":[0.00002006844,0.0000143839,0.0003162448,0.00001949133,0.000010804,0.00007626901,0.00004250316,0.9861356,0.000258344,0.01070439,0.0001412599,0.002260526],"study_design_scores_gemma":[0.000005265694,0.00001366776,0.0000837156,0.000003836837,0.000006249994,0.00001111006,0.00001969524,0.9953296,0.00006260102,0.004236578,0.000223043,0.000004564907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1084016,0.000414768,0.8669033,0.0005070114,0.00005889381,0.0001845079,0.0005253793,0.0002408342,0.02276376],"genre_scores_gemma":[0.9352438,0.0003949213,0.05542678,0.00005344059,0.0000272214,0.0002057988,0.0001827674,0.00002919166,0.008436134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04491804,"threshold_uncertainty_score":0.08931315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343272255046572,"score_gpt":0.2145990230963186,"score_spread":0.2011663005458529,"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."}}