{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006569666,0.0001260217,0.0002086066,0.0002142662,0.0001483328,0.000129705,0.00006678504,0.00004613466,0.000002582874],"category_scores_gemma":[0.00008954774,0.00009664555,0.00005548191,0.00003415303,0.00002292661,0.0004573056,0.00002074078,0.0001538787,1.642675e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009710752,"about_ca_system_score_gemma":0.000007050472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000144029,"about_ca_topic_score_gemma":0.000001994339,"domain_scores_codex":[0.9990631,0.0000745139,0.0003411933,0.0001281328,0.0002583165,0.0001347088],"domain_scores_gemma":[0.9993026,0.00004339086,0.0003213264,0.0001579901,0.0001376559,0.00003698403],"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":[0.00008144823,0.00002107896,0.0008970832,0.0002161802,0.00005807367,0.000005006791,0.000481243,0.9742177,0.009046143,0.000002359771,0.000005972862,0.01496767],"study_design_scores_gemma":[0.0005750956,0.0002083665,0.007392726,0.0004108766,0.0001187959,0.000007415485,0.00005832188,0.9783324,0.01267934,0.00002871927,0.00008487432,0.0001030451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7485946,0.00001737669,0.2506291,0.00008157131,0.0004091992,0.0002257986,3.861214e-7,0.00001966876,0.00002227364],"genre_scores_gemma":[0.9839317,9.111983e-7,0.01567902,0.000005154397,0.0003351702,0.000002679542,0.000003210103,0.00002266685,0.00001954405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2353371,"threshold_uncertainty_score":0.3941091,"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."}}