{"id":"W2925837702","doi":"10.1155/2019/1812543","title":"An Integrated Problem of <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math>-Hub Location and Revenue Management with Multiple Capacity Levels under Disruptions","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Revenue; Profit maximization; Profit (economics); Computer science; Stochastic programming; Mathematical optimization; Star (game theory); Algorithm; Operations research; Mathematics; Economics; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000354985,0.0001107758,0.00008992178,0.0001019422,0.00007879695,0.00004389647,0.0001131947,0.0001254314,0.0001890749],"category_scores_gemma":[0.00002853243,0.0001467727,0.00007225979,0.000239774,0.00006120928,0.000600339,0.000006147713,0.0002182345,0.000007529239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001014297,"about_ca_system_score_gemma":0.00005850574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003384917,"about_ca_topic_score_gemma":0.0001826947,"domain_scores_codex":[0.9989228,0.00003939171,0.00043064,0.0001486587,0.0002927758,0.0001657417],"domain_scores_gemma":[0.9991591,0.00008022897,0.0003956163,0.0001843584,0.0001008238,0.0000798969],"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":[0.0001283115,0.00004329269,0.0001039318,0.0004398528,0.0001356938,0.00001459874,0.001546706,0.7934037,0.007419238,0.1945642,0.00002991329,0.002170544],"study_design_scores_gemma":[0.001790805,0.0006306549,0.02609144,0.001164777,0.0003692475,0.00009005694,0.002701719,0.8201862,0.1462419,0.0001941487,0.0001920494,0.0003469427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8235091,0.00004564757,0.1756111,0.00003245465,0.0001324242,0.00002916244,0.00003019669,0.00002849947,0.0005814121],"genre_scores_gemma":[0.8873466,0.0000987426,0.1123224,0.00002444025,0.00004074188,0.0000231295,0.00008201347,0.00004923328,0.00001274993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1943701,"threshold_uncertainty_score":0.5985219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01523716657415934,"score_gpt":0.2449827766764912,"score_spread":0.2297456101023319,"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."}}