{"id":"W1995889581","doi":"10.1007/s11269-011-9953-4","title":"A Hybrid Dynamic Dual Interval Programming for Irrigation Water Allocation under Uncertainty","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Interval (graph theory); Dual (grammatical number); Mathematical optimization; Irrigation; Dynamic programming; Computer science; Reliability (semiconductor); Operations research; Process (computing); Irrigation district; Water scarcity; Water resources; Economic shortage; Mathematics","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":[0.0018704,0.0007527557,0.001432657,0.0006939727,0.0003435601,0.001676072,0.001652903,0.001477904,0.004323772],"category_scores_gemma":[0.002483018,0.0008117015,0.0008809936,0.001063348,0.0006572914,0.001171519,0.001456995,0.001386998,0.0002955852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009379543,"about_ca_system_score_gemma":0.001173471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002957408,"about_ca_topic_score_gemma":0.001838811,"domain_scores_codex":[0.999347,0.0003067073,0.00002226448,0.00009355979,0.0001434029,0.00008697293],"domain_scores_gemma":[0.9991891,0.0005369787,0.00006487266,0.00003408285,0.0001242517,0.00005068097],"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":[0.00005255807,0.00005183638,0.0001103559,0.00005049102,0.00002576897,0.00002711879,0.00002032001,0.9755671,0.0005034902,0.009401035,0.0005422776,0.01364764],"study_design_scores_gemma":[0.000006028444,0.00001546187,0.00001562175,0.000003097571,0.000003523857,0.000003457192,0.000002458322,0.9985843,0.00005552601,0.001162678,0.0001453228,0.000002567107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01588761,0.0002177245,0.9779661,0.0001924377,0.00006144821,0.00005353315,0.00006328691,0.00009159564,0.005466204],"genre_scores_gemma":[0.69073,0.0004670404,0.3014732,0.0001996841,0.0001398961,0.000400316,0.000193793,0.0001284633,0.006267606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004323772,"threshold_uncertainty_score":0.0144645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014230295117238,"score_gpt":0.2115161466185118,"score_spread":0.2013738436673394,"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."}}