{"id":"W2804907487","doi":"10.1029/2017wr022105","title":"Copula‐Based Chance‐Constrained Hydro‐Economic Optimization Model for Optimal Design of Reservoir‐Irrigation District Systems under Multiple Interdependent Sources of Uncertainty","year":2018,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water resources management and optimization","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Irrigation; Copula (linguistics); Interdependence; Inflow; Environmental science; Optimal design; Water resource management; Agricultural engineering; Econometrics; Economics; Mathematics; Statistics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001669162,0.0002486423,0.0003699201,0.0006309462,0.0002017549,0.0001431165,0.0005420274,0.0001431,0.0000492159],"category_scores_gemma":[0.00004740393,0.0002070217,0.0001078109,0.0002290611,0.0003499338,0.000211221,0.0001483841,0.0001643489,0.00001182601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002196192,"about_ca_system_score_gemma":0.00002348379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000216904,"about_ca_topic_score_gemma":0.00003293144,"domain_scores_codex":[0.997489,0.0002882913,0.0007187633,0.0003860636,0.0005168656,0.0006009912],"domain_scores_gemma":[0.9988117,0.0001803774,0.000135731,0.0004339807,0.0003342295,0.0001040203],"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":[0.0003375468,0.00004395722,0.0001398007,0.0003945536,0.00009292505,7.349796e-7,0.002628086,0.993529,0.002507486,0.00004630278,0.0001558475,0.0001237552],"study_design_scores_gemma":[0.001227124,0.0002816974,0.00002100426,0.0001367043,0.00002673811,6.31257e-7,0.0005224457,0.979364,0.01786308,0.00008002524,0.0002595202,0.0002169767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3487667,0.00006002651,0.6493698,0.00003929014,0.00008577596,0.001087201,0.00006788274,0.00009245874,0.0004309129],"genre_scores_gemma":[0.9908143,0.00001114425,0.008063068,0.000003976802,0.0001579086,0.0001797964,0.0003076145,0.00007745501,0.0003847015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6420476,"threshold_uncertainty_score":0.8442099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06624994031761383,"score_gpt":0.286169513583776,"score_spread":0.2199195732661621,"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."}}