{"id":"W2084913131","doi":"10.1016/j.ejor.2008.12.019","title":"Enhanced-interval linear programming","year":2008,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Water resources management and optimization","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Interval (graph theory); Linear programming; Mathematical optimization; Stochastic programming; Linear-fractional programming; Mathematics; Fuzzy logic; Goal programming; Constraint programming; Computer science; Constraint (computer-aided design); Constraint satisfaction; Extension (predicate logic); Probabilistic logic; Artificial intelligence; Statistics","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":[0.001299171,0.00006632314,0.00008472983,0.0002261318,0.0001528966,0.00007110579,0.0002368491,0.000010238,0.0001362759],"category_scores_gemma":[0.00008862,0.000055853,0.0000494811,0.000200407,0.00005595294,0.0002601474,0.00004810979,0.0003132682,0.0001701164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000042698,"about_ca_system_score_gemma":0.00002235585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.871366e-7,"about_ca_topic_score_gemma":3.246313e-7,"domain_scores_codex":[0.9986754,0.0002089781,0.0002908167,0.00006715677,0.0005728776,0.0001847382],"domain_scores_gemma":[0.9994066,0.00003415813,0.00002743536,0.0000760959,0.0003752266,0.00008046479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001220276,0.0001325822,0.0005815343,0.00005684338,0.0001653767,0.0009791474,0.003879364,0.9266512,0.01031286,0.0008987734,0.02837255,0.02784769],"study_design_scores_gemma":[0.002627709,0.001422816,0.01463516,0.0003000307,0.00001991985,0.000502215,0.0005072056,0.176699,0.02004118,0.00004043994,0.7826481,0.0005561481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7637619,0.0004308087,0.1593645,0.0004010732,0.0003562914,0.0002139632,0.000001539777,0.00007686138,0.0753931],"genre_scores_gemma":[0.9883144,0.000155731,0.009247496,0.00001750943,0.0006164853,0.000001114172,0.000005110444,0.00002769134,0.001614444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7542756,"threshold_uncertainty_score":0.2277619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07324067857832808,"score_gpt":0.3051836425078392,"score_spread":0.2319429639295112,"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."}}