{"id":"W2770173738","doi":"10.1007/s00477-017-1490-0","title":"Risk aversion based interval stochastic programming approach for agricultural water management under uncertainty","year":2017,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Water resources management and optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China; Beijing Normal University","keywords":"CVAR; Expected shortfall; Stochastic programming; Risk measure; Interval (graph theory); Mathematical optimization; Risk aversion (psychology); Time horizon; Context (archaeology); Risk management; Computer science; Measure (data warehouse); Stochastic game; Downside risk; Linear programming; Expected utility hypothesis; Econometrics; Operations research; Mathematics; Economics; Statistics; Mathematical economics; Data mining","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":[0.001965923,0.0009320172,0.001413972,0.0007565835,0.0003445515,0.001498943,0.002020353,0.001622236,0.003306949],"category_scores_gemma":[0.003385955,0.0006544648,0.001305816,0.000958564,0.0007427566,0.001443963,0.001262195,0.002428077,0.0001849889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295827,"about_ca_system_score_gemma":0.001571144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005711683,"about_ca_topic_score_gemma":0.003381446,"domain_scores_codex":[0.9992786,0.0003560135,0.0000245598,0.00009428938,0.0001650225,0.00008156362],"domain_scores_gemma":[0.9986063,0.0009996324,0.0001041635,0.00003642763,0.0001830107,0.00007061392],"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":[0.00001222116,0.00003187379,0.0001007859,0.00003282889,0.00003116686,0.00003101118,0.00002195372,0.9672461,0.0002239738,0.02745887,0.0003704429,0.004438782],"study_design_scores_gemma":[0.000002561822,0.00001034285,0.00002512578,0.00000381316,0.000005658302,0.000003515917,0.000003967593,0.991944,0.00002748087,0.007824608,0.0001459297,0.000003020367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01130966,0.000332504,0.9837766,0.0002780465,0.00006570542,0.00003104939,0.00005921284,0.00005120692,0.004095913],"genre_scores_gemma":[0.7732487,0.001204545,0.2107964,0.0002932315,0.0002436168,0.0004046933,0.0002307323,0.0001542983,0.01342389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005711683,"threshold_uncertainty_score":0.01135683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01958765054335631,"score_gpt":0.2733434129686158,"score_spread":0.2537557624252594,"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."}}