{"id":"W1967987311","doi":"10.1016/j.cor.2010.01.004","title":"The quadratic minimum spanning tree problem: A lower bounding procedure and an efficient search algorithm","year":2010,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Galatasaray Üniversitesi","keywords":"Bounding overwatch; Minimum spanning tree; Tabu search; Spanning tree; Mathematics; Quadratic equation; k-minimum spanning tree; Heuristic; Mathematical optimization; Euclidean minimum spanning tree; Relaxation (psychology); Distributed minimum spanning tree; Local search (optimization); Algorithm; Kruskal's algorithm; Combinatorics; Computer science; Tree structure; Binary tree; Artificial intelligence","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":[0.003059756,0.001458695,0.001784766,0.001704375,0.0009533273,0.001729339,0.002014174,0.002173579,0.005982831],"category_scores_gemma":[0.01055406,0.0008642652,0.001352167,0.003057052,0.001224202,0.00384171,0.002556513,0.003293169,0.001361825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131324,"about_ca_system_score_gemma":0.001649072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002855182,"about_ca_topic_score_gemma":0.003196013,"domain_scores_codex":[0.9986171,0.0005530927,0.00005509424,0.000177833,0.0004944396,0.0001024513],"domain_scores_gemma":[0.9971317,0.002141381,0.0001284192,0.0002034243,0.0003239335,0.00007118217],"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":[0.0001709531,0.0002366677,0.000337816,0.0004034239,0.0000575887,0.0001457693,0.0001974843,0.5404879,0.00510691,0.2195599,0.01638794,0.2169076],"study_design_scores_gemma":[0.00001736186,0.00002995741,0.00008529045,0.00002103023,0.00001479141,0.00006815697,0.00001441947,0.9565206,0.0005201585,0.04008562,0.002612985,0.000009653992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001942661,0.0003386147,0.9945801,0.0002130422,0.000051594,0.00004847075,0.00004773326,0.0001181663,0.002659584],"genre_scores_gemma":[0.07808316,0.0009675185,0.9139772,0.0001666526,0.0002099996,0.0003598607,0.0003553655,0.000306414,0.00557373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005982831,"threshold_uncertainty_score":0.02001452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0356672783289809,"score_gpt":0.3615242661527958,"score_spread":0.3258569878238148,"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."}}