{"id":"W2102937943","doi":"10.5194/hess-18-1793-2014","title":"A dual-inexact fuzzy stochastic model for water resources management and non-point source pollution mitigation under multiple uncertainties","year":2014,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Water resources management and optimization","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Mathematical optimization; Stochastic programming; Computer science; Linear programming; Interval (graph theory); Fuzzy logic; Interior point method; Robustness (evolution); Operations research; Mathematics","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":[0.0004715017,0.0001316724,0.0001485162,0.0001687257,0.0003436588,0.00009746126,0.0000673618,0.00006143603,0.000001537139],"category_scores_gemma":[0.000004307382,0.00009682001,0.00002303708,0.00007074384,0.0001616948,0.00017658,0.00004543949,0.00003873579,0.000004520236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000100085,"about_ca_system_score_gemma":0.00000135217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001752193,"about_ca_topic_score_gemma":0.00003360598,"domain_scores_codex":[0.9991624,0.00003386441,0.000176718,0.0002463319,0.0001089808,0.0002716476],"domain_scores_gemma":[0.9997861,0.00003535966,0.00003406132,0.00008469551,0.00001364529,0.00004613637],"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":[0.00001296992,0.000003132154,0.0002308831,0.000199778,0.00002055376,2.002148e-7,0.001388173,0.9968206,0.0001341036,0.0007911514,0.00002826701,0.00037021],"study_design_scores_gemma":[0.0004191563,0.00007823138,0.0005442534,0.00004367409,0.00003272768,0.000004928456,0.0007770079,0.9969943,0.0001005767,0.0006647091,0.0002103124,0.0001301525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5582402,0.00006553897,0.440493,0.0001265165,0.00007572858,0.0002495844,0.000001379634,0.00008879804,0.0006592307],"genre_scores_gemma":[0.9982668,0.000008258207,0.001165779,0.00005629049,0.00005340042,0.00004508512,0.000009312497,0.00001002419,0.0003850758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4400266,"threshold_uncertainty_score":0.3948205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008861189740773115,"score_gpt":0.1837540049412669,"score_spread":0.1748928152004938,"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."}}