{"id":"W2082434835","doi":"10.1016/j.wasman.2008.12.011","title":"Inexact fuzzy-stochastic constraint-softened programming – A case study for waste management","year":2009,"lang":"en","type":"article","venue":"Waste Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Constraint (computer-aided design); Mathematical optimization; Interval (graph theory); Context (archaeology); Fuzzy logic; Stochastic programming; Range (aeronautics); Constraint satisfaction; Fuzzy set; Constraint programming; Tree (set theory); Random variable; Computer science; Operations research; Mathematics; Engineering; Probabilistic logic; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002095929,0.0006203283,0.001057981,0.0005352964,0.001063631,0.001814069,0.001153048,0.003046053,0.00369356],"category_scores_gemma":[0.005291996,0.000432937,0.0007691247,0.001363684,0.001293311,0.001117023,0.001085748,0.001520655,0.0001735422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431928,"about_ca_system_score_gemma":0.001622796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01006267,"about_ca_topic_score_gemma":0.01306582,"domain_scores_codex":[0.9990951,0.0003789049,0.00004370912,0.0001023484,0.0002725713,0.000107398],"domain_scores_gemma":[0.996415,0.002843187,0.000163144,0.0001863882,0.0002618902,0.0001303609],"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":[0.00009416338,0.0001535907,0.0005943431,0.00006422205,0.00002047715,0.0007594931,0.0000945662,0.9783604,0.0007765709,0.008001404,0.0003609297,0.0107198],"study_design_scores_gemma":[0.00001937283,0.00004629076,0.0002335378,0.000006313504,0.000008763953,0.00007982467,0.00005646044,0.9937034,0.0007455945,0.004681309,0.0004107465,0.000008323103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5804069,0.0003874883,0.403914,0.0009701153,0.00005482294,0.0001513342,0.0002848802,0.0002846851,0.01354578],"genre_scores_gemma":[0.9153467,0.0001225301,0.07975335,0.00005260967,0.00001298601,0.00007930055,0.000107457,0.00004721426,0.004477839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01006267,"threshold_uncertainty_score":0.02000821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01257451400504054,"score_gpt":0.222644798912678,"score_spread":0.2100702849076375,"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."}}