{"id":"W1884874776","doi":"10.1029/2003wr002828","title":"Fuzzy criteria for the evaluation of water resource systems performance","year":2004,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water resources management and optimization","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Catastrophic Loss Reduction","keywords":"Fuzzy logic; Robustness (evolution); Sustainability; Reliability (semiconductor); Computer science; Vulnerability (computing); Fuzzy set; Index (typography); Water resources; Reliability engineering; Resource (disambiguation); Risk analysis (engineering); Environmental resource management; Operations research; Engineering; Environmental science; Business; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004861037,0.0001746165,0.000190639,0.0003335249,0.0003587753,0.0002376805,0.0005566429,0.00009365291,0.00005978755],"category_scores_gemma":[0.00002929869,0.0000909847,0.0000732889,0.0002114432,0.0001491642,0.0001853689,0.0001685833,0.0002196094,0.00008312867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001532206,"about_ca_system_score_gemma":0.000006968604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007162368,"about_ca_topic_score_gemma":0.000006498866,"domain_scores_codex":[0.9972309,0.000206633,0.0003776266,0.0002516647,0.001231365,0.0007018503],"domain_scores_gemma":[0.9989378,0.00007154979,0.0000216505,0.0005113105,0.0003956499,0.00006200503],"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":[0.00007428147,0.00002163593,0.00006210746,0.0004224958,0.00007421608,6.140194e-7,0.008571047,0.9794179,0.008381626,0.00005550928,0.0008549134,0.002063704],"study_design_scores_gemma":[0.001969995,0.0002115318,0.0005700091,0.0001662235,0.00009885541,0.000004463326,0.001385018,0.6488457,0.1126474,0.0005780616,0.2332142,0.0003085466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854449,0.0006301673,0.001121809,0.0003570686,0.0001961333,0.001709662,0.000007189441,0.0001239064,0.01040918],"genre_scores_gemma":[0.9978511,0.00004960387,0.00009869733,0.000008993956,0.0002918158,0.0004490178,0.00007322874,0.00006104622,0.001116454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3305722,"threshold_uncertainty_score":0.3710248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09229382273695184,"score_gpt":0.3296450434642224,"score_spread":0.2373512207272705,"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."}}