{"id":"W2805325553","doi":"10.1007/s00453-018-0458-x","title":"LP-Based Approximation Algorithms for Facility Location in Buy-at-Bulk Network Design","year":2018,"lang":"en","type":"article","venue":"Algorithmica","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Facility location problem; Theory of computation; Routing (electronic design automation); Computer science; Network planning and design; Approximation algorithm; Linear programming; Upper and lower bounds; Mathematical optimization; 1-center problem; Integer programming; Mathematics; Algorithm; Computer network","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":[0.006331009,0.002713848,0.003660611,0.001960748,0.001110924,0.003016655,0.003952489,0.003617965,0.01213044],"category_scores_gemma":[0.02405669,0.001963619,0.001918338,0.002738326,0.002369985,0.004887882,0.003219372,0.005917663,0.001465401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003130588,"about_ca_system_score_gemma":0.00328082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00787468,"about_ca_topic_score_gemma":0.008352615,"domain_scores_codex":[0.9975575,0.001461007,0.00008321044,0.0002779305,0.0003375951,0.0002826141],"domain_scores_gemma":[0.9820564,0.01564577,0.0004598858,0.0006084563,0.000765184,0.000464355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001722151,0.0001802734,0.0005114891,0.0002352471,0.00006020211,0.0000481261,0.0000895554,0.9169263,0.0002586322,0.04328722,0.005970261,0.0322605],"study_design_scores_gemma":[0.00002324339,0.00002099569,0.00002902278,0.00001409451,0.00001228061,0.000007909607,0.00001571658,0.9725409,0.00007111474,0.02686949,0.0003907907,0.00000446624],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005923948,0.0004963301,0.9885133,0.0005735431,0.00007289575,0.00007628409,0.0001597617,0.000221995,0.0039619],"genre_scores_gemma":[0.3254711,0.001347253,0.657962,0.0006461486,0.0004179218,0.0009163091,0.001078341,0.0005482676,0.01161274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01213044,"threshold_uncertainty_score":0.04058039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05242191817233216,"score_gpt":0.2605518416760623,"score_spread":0.2081299235037302,"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."}}