{"id":"W1495726324","doi":"10.1029/2007wr006192","title":"Shortcomings of linear programming in optimizing river basin allocation","year":2008,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water resources management and optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Linear programming; Convergence (economics); Mathematical optimization; Routing (electronic design automation); Outflow; Process (computing); Iterative and incremental development; Simple (philosophy); Simplex algorithm; Flow (mathematics); Algorithm; Mathematics; Geology","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":[0.004187078,0.00108512,0.001118414,0.0006120183,0.0005445096,0.002598157,0.0008865196,0.001517642,0.002955123],"category_scores_gemma":[0.01352871,0.0007993513,0.0007013261,0.001962965,0.001556729,0.002377468,0.00131826,0.00251494,0.0007528942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321697,"about_ca_system_score_gemma":0.002838778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01174427,"about_ca_topic_score_gemma":0.008495214,"domain_scores_codex":[0.9974964,0.001608548,0.00008618347,0.0002131516,0.0004984459,0.00009718075],"domain_scores_gemma":[0.9943763,0.004807251,0.0002177222,0.0001762065,0.0003716205,0.00005083229],"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":[0.00003077395,0.00003396618,0.0003367678,0.0001563002,0.00002775233,0.00003502834,0.00006161354,0.8923468,0.0001829386,0.05819209,0.001635786,0.04696023],"study_design_scores_gemma":[0.000009340595,0.00002524391,0.00005358686,0.00003613255,0.000006218132,0.00001712532,0.00002387785,0.9640211,0.0001631266,0.03296221,0.002675168,0.000006938402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01509295,0.003907285,0.9492188,0.00392402,0.0001719091,0.00007288177,0.0001047534,0.0003520702,0.02715533],"genre_scores_gemma":[0.4877494,0.008553981,0.4886644,0.001090194,0.000501857,0.000373869,0.000217177,0.0005303493,0.01231878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01174427,"threshold_uncertainty_score":0.02335179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04611013632361856,"score_gpt":0.2755897659002688,"score_spread":0.2294796295766502,"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."}}