{"id":"W2078462043","doi":"10.1080/07011784.2014.985514","title":"Application of a goal programming algorithm to incorporate environmental requirements in a multi-objective Columbia River Treaty Reservoir optimization model","year":2015,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Water resources management and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Hydro (Canada); University of British Columbia","funders":"","keywords":"Flexibility (engineering); Computer science; Goal programming; Treaty; Lexicographical order; Mathematical optimization; Operations research; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001350469,0.0008712246,0.000750471,0.0006404672,0.0006019082,0.001288462,0.001310442,0.001690184,0.002285509],"category_scores_gemma":[0.001873447,0.0006192279,0.0007843359,0.0008085781,0.0006952904,0.0006438782,0.0009768109,0.00136661,0.0002237046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001688521,"about_ca_system_score_gemma":0.003127314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05293098,"about_ca_topic_score_gemma":0.03803318,"domain_scores_codex":[0.9996269,0.0001678853,0.00001232769,0.0000523913,0.00007883015,0.00006167909],"domain_scores_gemma":[0.9991508,0.0005746902,0.00006426378,0.00001982533,0.0001568988,0.00003354788],"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":[0.000003347463,0.000003758729,0.00006675799,0.0000043915,0.000002856187,0.00001240619,0.000004743374,0.9978685,0.00003454824,0.001090903,0.00008284501,0.0008248776],"study_design_scores_gemma":[0.000002728478,0.000003206576,0.00001964596,0.000001660557,0.000001479658,0.000001480117,0.000002872802,0.9993688,0.00003049138,0.0004638403,0.0001024407,0.000001275239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06283396,0.0001653784,0.9193239,0.0005272321,0.00003592756,0.0001722775,0.000322948,0.0003656494,0.01625274],"genre_scores_gemma":[0.6437632,0.000202266,0.3463489,0.0001751462,0.0000255413,0.0007038965,0.0004421412,0.0001328049,0.008206172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05293098,"threshold_uncertainty_score":0.1052458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01990363124841878,"score_gpt":0.2031471686698928,"score_spread":0.183243537421474,"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."}}