{"id":"W2078042127","doi":"10.1016/j.resconrec.2009.02.002","title":"SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty","year":2009,"lang":"en","type":"article","venue":"Resources Conservation and Recycling","topic":"Water resources management and optimization","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Robustness (evolution); Stochastic programming; Mathematical optimization; Municipal solid waste; Reliability (semiconductor); Constraint (computer-aided design); Robust optimization; Computer science; Risk analysis (engineering); Operations research; Reliability engineering; Engineering; Mathematics; Business; Waste management","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":[0.0002235292,0.0002084885,0.0002029204,0.0001829892,0.0002181026,0.0001525624,0.0001181565,0.00008483001,0.000003534591],"category_scores_gemma":[0.00001170139,0.0002032196,0.0000623416,0.0001832907,0.00004154801,0.0001425843,0.00002909819,0.0001024145,9.584894e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004187499,"about_ca_system_score_gemma":0.000003591299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005948439,"about_ca_topic_score_gemma":0.00002050882,"domain_scores_codex":[0.9989119,0.00001733183,0.0003307018,0.0002543096,0.0001455085,0.0003402396],"domain_scores_gemma":[0.999608,0.00004368294,0.00007625749,0.0001552141,0.00003947024,0.00007732874],"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":[0.00004167431,0.00001724582,0.00001530305,0.0001161704,0.00005514998,8.503256e-7,0.001496407,0.9815661,0.0001275984,0.000721225,0.00005598625,0.01578623],"study_design_scores_gemma":[0.0008881759,0.00003883789,0.00006179587,0.0001566007,0.00006624959,0.000001419046,0.001148494,0.9958888,0.0000300473,0.0006029364,0.0008674246,0.0002491907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3580627,0.0001836834,0.6396454,0.0004541684,0.00006948087,0.0006938787,0.000003303054,0.0003089933,0.0005783763],"genre_scores_gemma":[0.976072,0.00009463972,0.02220076,0.0004021848,0.0001083611,0.00007483411,0.00006722839,0.00003374586,0.0009462425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6180093,"threshold_uncertainty_score":0.8287054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02850189190950547,"score_gpt":0.2381242054750112,"score_spread":0.2096223135655057,"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."}}