{"id":"W2024687844","doi":"10.1155/2013/482095","title":"An Inventory-Theory-Based Inexact Multistage Stochastic Programming Model for Water Resources Management","year":2013,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Water resources management and optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Program for Changjiang Scholars and Innovative Research Team in University; National Key Research and Development Program of China; State Key Laboratory of Hydroscience and Engineering; National Natural Science Foundation of China","keywords":"Economic shortage; Mathematical optimization; Stochastic programming; Computer science; Context (archaeology); Dynamic programming; Operations research; Inventory management; Mathematics; Operations management; Engineering","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":[0.0003717361,0.0002693235,0.0002361866,0.0002932088,0.00004041795,0.0001650287,0.0002477946,0.00008142272,0.00004681518],"category_scores_gemma":[0.00001569255,0.0002252016,0.0000602346,0.0001235313,0.00001935648,0.0002750426,0.00004357657,0.0001359153,0.00003088837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008456512,"about_ca_system_score_gemma":0.000001059678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001839131,"about_ca_topic_score_gemma":0.000001494312,"domain_scores_codex":[0.9986144,0.00001345505,0.0004100663,0.0002461491,0.0001772893,0.0005386287],"domain_scores_gemma":[0.9995342,0.00003962357,0.00002208899,0.0002779393,0.00002124999,0.0001049226],"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":[0.000003444188,0.00007195384,0.00001072016,0.002164906,0.00002835275,0.000001229715,0.001487955,0.9934462,0.0004732264,0.00148134,0.00001463313,0.0008160484],"study_design_scores_gemma":[0.0005539243,0.00002907816,0.000005845955,0.0002275081,0.00002390266,3.865105e-7,0.00005173174,0.9935473,0.0001996194,0.004920549,0.0001305816,0.0003096175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04798061,0.00002059031,0.9494866,0.00001274923,0.00004305835,0.00164812,0.000001628001,0.0005821314,0.0002244793],"genre_scores_gemma":[0.92044,8.040777e-7,0.07782839,0.00001217518,0.00002916904,0.001335224,0.00003265443,0.0001053976,0.0002161473],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8724594,"threshold_uncertainty_score":0.9183453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01097068955259676,"score_gpt":0.2008256065170339,"score_spread":0.1898549169644372,"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."}}