{"id":"W2070620685","doi":"10.1089/ees.2007.0241.pta","title":"Inexact Minimax Regret Integer Programming for Long-Term Planning of Municipal Solid Waste Management—Part A: Methodology Development","year":2009,"lang":"en","type":"article","venue":"Environmental Engineering Science","topic":"Water resources management and optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regret; Mathematical optimization; Interval (graph theory); Minimax; Context (archaeology); Computer science; Term (time); Linear programming; Integer programming; Operations research; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004314595,0.0002030628,0.0001967154,0.0002850721,0.00008564643,0.00004226837,0.0003771122,0.00004293823,0.00001148066],"category_scores_gemma":[0.0000124508,0.0002096223,0.00004586625,0.0002311164,0.00008655815,0.0002726596,0.0001010845,0.00009002221,0.00000385451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001702307,"about_ca_system_score_gemma":0.00000468158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.862574e-7,"about_ca_topic_score_gemma":1.394225e-7,"domain_scores_codex":[0.9987058,0.000007802556,0.0003174861,0.0002763691,0.0002459361,0.00044661],"domain_scores_gemma":[0.9996274,0.00002599705,0.00005475534,0.0002110776,0.000004342405,0.00007649933],"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":[0.00001309676,0.00003606163,0.0009459701,0.0001273543,0.0000411194,0.000007754976,0.001648214,0.9690815,0.01009065,0.00007987701,0.00001473002,0.01791372],"study_design_scores_gemma":[0.001340222,0.0003117239,0.04349214,0.0004841205,0.00009613096,0.00001562426,0.0005855261,0.7891909,0.1558303,0.00002213579,0.00754372,0.001087363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6828608,0.0001625695,0.3158864,0.000007848565,0.0002300151,0.000390702,0.000001841673,0.0001415947,0.0003182227],"genre_scores_gemma":[0.8981396,0.00001610451,0.1015073,0.00001149952,0.00004043003,0.00003697242,0.00002532698,0.00002376922,0.0001990017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2152788,"threshold_uncertainty_score":0.8548148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02649217595395565,"score_gpt":0.2523131972582814,"score_spread":0.2258210213043258,"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."}}