{"id":"W4385705217","doi":"10.1007/s10732-023-09515-w","title":"An integrated learning and progressive hedging matheuristic for stochastic network design problem","year":2023,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematical optimization; Quality (philosophy); Network planning and design; Point (geometry); Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002153364,0.001009731,0.001692085,0.0009073244,0.0005153654,0.001116445,0.002023395,0.001796319,0.0044325],"category_scores_gemma":[0.00427722,0.0006574212,0.0008458098,0.0007212912,0.0009162897,0.001578392,0.00204307,0.00180604,0.0002664576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267388,"about_ca_system_score_gemma":0.00164969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005934069,"about_ca_topic_score_gemma":0.005718991,"domain_scores_codex":[0.9994286,0.0002300625,0.00002809784,0.0001096678,0.0001241242,0.00007942797],"domain_scores_gemma":[0.9983626,0.001141758,0.00009655314,0.0000919945,0.0001948988,0.0001122121],"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":[0.00005314148,0.00009710943,0.000327525,0.00006012854,0.00002785826,0.0000393544,0.00003118157,0.9616297,0.0004365882,0.0113953,0.0006666504,0.02523539],"study_design_scores_gemma":[0.000008861391,0.00002551651,0.00003422646,0.000003425966,0.000005743035,0.00000551121,0.00000244528,0.9964086,0.00007164046,0.003309813,0.0001219711,0.000002203382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03008617,0.0002417537,0.9638143,0.0002921064,0.00004417968,0.0001181507,0.00007132351,0.000144801,0.005187212],"genre_scores_gemma":[0.7075735,0.0003529963,0.2841118,0.000222813,0.0001102175,0.000312761,0.0001841547,0.00006999617,0.007061779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005934069,"threshold_uncertainty_score":0.01482821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02906346457009566,"score_gpt":0.3317644664010722,"score_spread":0.3027010018309765,"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."}}