{"id":"W2734889840","doi":"10.1287/ijoc.2017.0747","title":"Numerically Safe Lower Bounds for the Capacitated Vehicle Routing Problem","year":2017,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Branch and cut; Pruning; Branch and bound; Heuristic; Mathematics; Linear programming relaxation; Integer programming; Steiner tree problem; Linear programming; Routing (electronic design automation); Upper and lower bounds; Function (biology); Integer (computer science); Key (lock); Vehicle routing problem; Dual (grammatical number); Computer science","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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002011695,0.0002304788,0.0002543039,0.00008311463,0.002456815,0.001269339,0.0007362517,0.00009950254,0.00002014353],"category_scores_gemma":[0.0007701501,0.0001603288,0.000170918,0.0001185618,0.00007851945,0.0004823856,0.00009000045,0.0007143318,0.00002515089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001567492,"about_ca_system_score_gemma":0.00005482162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006790829,"about_ca_topic_score_gemma":0.000001276433,"domain_scores_codex":[0.9982957,0.00003140693,0.0006623538,0.0001382144,0.0003122631,0.0005600509],"domain_scores_gemma":[0.9981149,0.0006193505,0.0004580663,0.0004257888,0.0002428165,0.0001390592],"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":[0.0000287635,0.00001230717,0.0008945066,0.00002209891,0.00009408012,0.000006404795,0.0006146203,0.8511844,0.0002905377,0.0005604748,0.0005980768,0.1456937],"study_design_scores_gemma":[0.0008002892,0.0001084879,0.003083563,0.0001656386,0.00002041641,0.0000827245,0.0001038653,0.9867198,0.0003802391,0.0003194856,0.007963393,0.0002520352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1105344,0.00005164079,0.8759997,0.0007254536,0.001959937,0.0003373283,0.000003438083,0.000275298,0.01011274],"genre_scores_gemma":[0.8895681,0.00001537715,0.1093215,0.0002845885,0.0006243599,0.000004422139,0.000001289116,0.00005758987,0.0001228124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7790337,"threshold_uncertainty_score":0.9997674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02471797659407317,"score_gpt":0.2872649552065073,"score_spread":0.2625469786124341,"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."}}