{"id":"W2056200673","doi":"10.1142/s0217595907001206","title":"CORRELATIONS IN STOCHASTIC PROGRAMMING: A CASE FROM STOCHASTIC SERVICE NETWORK DESIGN","year":2007,"lang":"en","type":"article","venue":"Asia Pacific Journal of Operational Research","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Flexibility (engineering); Robustness (evolution); Stochastic programming; Computer science; Network planning and design; Stochastic optimization; Mathematical optimization; Stochastic modelling; Stochastic process; Service (business); Mathematics; Economics","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":[0.00293468,0.0001158091,0.0001699305,0.000481992,0.0001951929,0.00009159102,0.0001481439,0.00009651553,0.0001485674],"category_scores_gemma":[0.0002634389,0.0001151292,0.0000418972,0.001516839,0.0000613622,0.0002973183,0.000008458867,0.0008180898,0.00003384574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001920221,"about_ca_system_score_gemma":0.0003332071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005916285,"about_ca_topic_score_gemma":0.000646252,"domain_scores_codex":[0.9979841,0.000100832,0.0007386168,0.0001283965,0.000660507,0.0003875644],"domain_scores_gemma":[0.997441,0.001161693,0.00005663935,0.0001397115,0.001040464,0.0001605129],"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":[0.00007142134,0.00008993542,0.000365075,0.000005768627,0.00004671629,0.0003427344,0.00165582,0.9918756,0.0003814658,0.002420088,0.0009730734,0.001772259],"study_design_scores_gemma":[0.006174695,0.0008130583,0.08107782,0.0007899505,0.0001323937,0.004598199,0.03581119,0.8550013,0.0002866954,0.008723103,0.005421742,0.001169905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1144992,0.0002233057,0.883525,0.0005615373,0.0002900146,0.0004421247,0.000017267,0.00002768463,0.0004139119],"genre_scores_gemma":[0.9822226,0.000004173458,0.01736606,0.00001822491,0.0002570726,0.00002315836,0.00004091008,0.00002130567,0.00004651119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8677234,"threshold_uncertainty_score":0.4694833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08042517883552801,"score_gpt":0.3470568399910585,"score_spread":0.2666316611555304,"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."}}