{"id":"W3084901588","doi":"10.1002/ird.2523","title":"Efficient and Economical Allocation of Irrigation Water under a Changing Environment: a Stochastic Multi‐Objective Nonlinear Programming Model*","year":2020,"lang":"en","type":"article","venue":"Irrigation and Drainage","topic":"Water resources management and optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"China Postdoctoral Science Foundation; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Postdoctoral Scientific Research Development Fund of Heilongjiang Province","keywords":"Irrigation; Water resources; Water scarcity; Optimal allocation; Resource allocation; Water resource management; Natural resource; Computer science; Stochastic programming; Nonlinear pricing; Environmental science; Environmental economics; Mathematical optimization; Economics; Mathematics; Ecology; Microeconomics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000827361,0.0007911171,0.001003832,0.0005145585,0.0005515582,0.001311119,0.001258302,0.001750603,0.001794232],"category_scores_gemma":[0.001343161,0.000590346,0.0008134429,0.0008430363,0.001007867,0.0007710753,0.0008422871,0.00099159,0.0001789213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001582834,"about_ca_system_score_gemma":0.001903481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02367546,"about_ca_topic_score_gemma":0.0115566,"domain_scores_codex":[0.9996269,0.0001533054,0.00001355304,0.00007013774,0.00006889985,0.00006721308],"domain_scores_gemma":[0.9994243,0.0003302319,0.00009179325,0.00001489672,0.00009876825,0.00004004834],"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":[0.000005347075,0.000004757406,0.00008078321,0.000006082327,0.000004297618,0.00001657148,0.000004503685,0.9980479,0.00008896695,0.001340083,0.00005600821,0.0003447015],"study_design_scores_gemma":[0.000002142894,0.000003819524,0.00002665734,7.395727e-7,0.000001403003,0.000001423853,0.000002302,0.9995852,0.00001624337,0.0003188673,0.00003988375,0.000001344102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1573478,0.0003799945,0.8216819,0.001076435,0.00007498082,0.0001349735,0.0005383643,0.0001793223,0.01858637],"genre_scores_gemma":[0.9693381,0.0002927286,0.02317932,0.00007855853,0.00002628748,0.000278537,0.0001617509,0.00002670608,0.006617971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02367546,"threshold_uncertainty_score":0.04707533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284490168539809,"score_gpt":0.190531750830403,"score_spread":0.1776868491450049,"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."}}