{"id":"W2204163060","doi":"10.1016/j.cor.2015.12.013","title":"The Steiner traveling salesman problem with online advanced edge blockages","year":2015,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Zhejiang Sci-Tech University; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Alberta Innovates - Technology Futures; Genome Canada; Southern Taiwan Science Park; Georgia Southern University","keywords":"Travelling salesman problem; Enhanced Data Rates for GSM Evolution; Computer science; Competitive analysis; Mathematical optimization; Upper and lower bounds; Online algorithm; Service (business); Steiner tree problem; Mathematics; Algorithm; Telecommunications; Economics","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":[0.0009218846,0.0007962913,0.001418192,0.0005342115,0.0006258931,0.001824832,0.001645305,0.001956426,0.008203189],"category_scores_gemma":[0.003772819,0.0007756164,0.0009419731,0.001350792,0.0007461339,0.00438877,0.001444569,0.001641692,0.000792967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008910915,"about_ca_system_score_gemma":0.001170561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002453377,"about_ca_topic_score_gemma":0.002284968,"domain_scores_codex":[0.9992168,0.0003133812,0.00003156077,0.0001506835,0.0001354761,0.0001521794],"domain_scores_gemma":[0.9983619,0.0009811411,0.0002251949,0.0001628462,0.0001137677,0.0001552147],"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":[0.0005118571,0.0002039264,0.0008584878,0.0002512609,0.00007872523,0.0003913045,0.00009176689,0.7816119,0.00287605,0.1626561,0.007010981,0.04345763],"study_design_scores_gemma":[0.00003821064,0.00009972373,0.0002575712,0.00001523734,0.00002042703,0.00009637556,0.00004756798,0.9251128,0.000554151,0.07168636,0.002060367,0.00001109914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2412381,0.0009712017,0.7247474,0.001117902,0.0001781244,0.0001478583,0.0008677866,0.0003307086,0.0304009],"genre_scores_gemma":[0.7972112,0.001101018,0.175898,0.0001411113,0.0001916542,0.0001396627,0.0008951807,0.0001759476,0.02424628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008203189,"threshold_uncertainty_score":0.0274424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08018762853346349,"score_gpt":0.3639540335422048,"score_spread":0.2837664050087413,"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."}}