{"id":"W2594735390","doi":"10.1016/j.jclepro.2017.02.100","title":"Assessment of uncertainty effects on crop planning and irrigation water supply using a Monte Carlo simulation based dual-interval stochastic programming method","year":2017,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Water resources management and optimization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; National Science Foundation","keywords":"Irrigation; Monte Carlo method; Environmental science; Interval (graph theory); Probability distribution; Water scarcity; Agricultural engineering; Water supply; Stochastic programming; Computer science; Agriculture; Water resource management; Environmental engineering; Mathematical optimization; Mathematics; Statistics; Engineering","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":[0.002592115,0.0006121636,0.001059426,0.0009997049,0.0004361811,0.001099531,0.0009879703,0.001219762,0.001390814],"category_scores_gemma":[0.006517974,0.0007914255,0.001016943,0.0007536598,0.0006097558,0.000885623,0.0008329957,0.001048883,0.00007836746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267785,"about_ca_system_score_gemma":0.00155451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.013721,"about_ca_topic_score_gemma":0.005245742,"domain_scores_codex":[0.9992238,0.0004264551,0.00002952561,0.0000814975,0.0001547409,0.00008389163],"domain_scores_gemma":[0.9936025,0.005224417,0.0004239202,0.0001548573,0.0004454235,0.0001489086],"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":[0.00002649475,0.00001653876,0.0002371322,0.000005271369,0.000009215429,0.000008702158,0.000003434764,0.9978887,0.0001218151,0.0008457219,0.00002411843,0.0008127478],"study_design_scores_gemma":[0.00000242077,0.000006111562,0.0000502473,8.342683e-7,0.000002606001,0.00000155105,8.989332e-7,0.9997551,0.00004549915,0.0001214206,0.0000117807,0.000001496502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5018694,0.0003715197,0.4883145,0.0004561924,0.00006648574,0.00009803005,0.0002624585,0.0003006607,0.008260665],"genre_scores_gemma":[0.9673547,0.00009237113,0.03162365,0.00003551876,0.00001634349,0.00005751267,0.0001105507,0.00003052748,0.0006788816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.013721,"threshold_uncertainty_score":0.02728224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02271838929339889,"score_gpt":0.3082339241215446,"score_spread":0.2855155348281457,"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."}}