{"id":"W1039155804","doi":"10.1007/s00477-015-1134-1","title":"A stochastic programming with imprecise probabilities model for planning water resources systems under multiple uncertainties","year":2015,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Water resources management and optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Equivalence (formal languages); Stochastic programming; Computer science; Mathematical optimization; Water resources; Stochastic modelling; Variety (cybernetics); Computational intelligence; Linear programming; Operations research; Mathematics; Artificial intelligence; Statistics","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":[0.0007124925,0.0002430934,0.00021822,0.0001858358,0.000335334,0.0002919035,0.0001493569,0.00006705055,0.000002668012],"category_scores_gemma":[0.00002062575,0.0001635167,0.00002927512,0.00006420873,0.0002812626,0.0002210275,0.0001524582,0.0002648443,0.000003375184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003361228,"about_ca_system_score_gemma":0.0000164479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006808422,"about_ca_topic_score_gemma":0.00002358271,"domain_scores_codex":[0.9980101,0.0000618274,0.0002385817,0.0003522756,0.000650541,0.0006866684],"domain_scores_gemma":[0.9993252,0.00016301,0.00003472464,0.0002054575,0.00003553513,0.0002360399],"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":[0.0001528911,0.00006810866,0.001193726,0.0001251298,0.0001004022,0.000001597687,0.004600785,0.9928403,0.00006161986,0.00006326383,0.00004426362,0.0007478654],"study_design_scores_gemma":[0.001235574,0.0006152334,0.0002306963,0.00009818413,0.00004223818,0.000002958781,0.01181682,0.9845004,0.00002209498,0.001028346,0.0001650341,0.0002423898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4269772,0.0003599226,0.5711567,0.00001621897,0.00004065462,0.001286904,0.00002832665,0.00007775019,0.00005632573],"genre_scores_gemma":[0.9918323,0.00002749274,0.006226698,0.000001948408,0.00006909576,0.001013856,0.00009192236,0.00006026812,0.0006764327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.56493,"threshold_uncertainty_score":0.6668016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04460933320416285,"score_gpt":0.2823717330864656,"score_spread":0.2377623998823028,"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."}}