{"id":"W4292533090","doi":"","title":"A Metaheuristic for Service Network Design with Revenue Management for Freight Intermodal Transport","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Revenue; Service (business); Computer science; Transport engineering; Revenue management; Metaheuristic; Traffic management; Business; Operations research; Engineering; Marketing; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003411854,0.000347087,0.0004300323,0.0000862841,0.0001460706,0.0001445975,0.0007343586,0.0001981662,0.00003013141],"category_scores_gemma":[0.0001125318,0.0003461165,0.0001376623,0.0001757988,0.00006097446,0.00005249015,0.0001894341,0.0003021146,0.000004319976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032281,"about_ca_system_score_gemma":0.00009398882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000142561,"about_ca_topic_score_gemma":0.0007829812,"domain_scores_codex":[0.9981017,0.0003549035,0.0004456155,0.0004887069,0.0002175359,0.0003916048],"domain_scores_gemma":[0.996545,0.0006243252,0.0001948412,0.001034956,0.00144321,0.0001576962],"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":[0.000494686,0.001000639,0.0005313687,0.01501602,0.002925509,0.00006499093,0.01075104,0.4923694,0.0001368913,0.2470507,0.1365562,0.09310254],"study_design_scores_gemma":[0.002363486,0.000003868573,0.0007588062,0.005294001,0.001239029,0.00001851096,0.0000913313,0.6542236,0.001706824,0.07025539,0.2624589,0.001586252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005857756,0.0007100872,0.9774339,0.0008062256,0.0002983137,0.001718108,0.0004232358,0.0002491658,0.01777516],"genre_scores_gemma":[0.1934192,0.0003157818,0.7935497,0.0001132208,0.0001278937,0.001449248,0.001927235,0.000178165,0.008919673],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1928334,"threshold_uncertainty_score":0.9998991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02502662176615957,"score_gpt":0.2146339073354761,"score_spread":0.1896072855693166,"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."}}