{"id":"W2053778299","doi":"10.1002/nav.20419","title":"A nested benders decomposition approach for telecommunication network planning","year":2010,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Benders' decomposition; Mathematical optimization; Decomposition method (queueing theory); Decomposition; Computer science; Network planning and design; Integer (computer science); Mathematics; Telecommunications; Discrete mathematics","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007203443,0.0002265069,0.0002864359,0.0003999982,0.00149259,0.0007801794,0.005339309,0.0003358208,0.00001140698],"category_scores_gemma":[0.002277021,0.0002285305,0.000109902,0.001707775,0.0005393359,0.0004502143,0.00188519,0.002579289,0.00003609853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001963004,"about_ca_system_score_gemma":0.0004633976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000853016,"about_ca_topic_score_gemma":0.00006494335,"domain_scores_codex":[0.9951419,0.001008619,0.0005040378,0.0006800914,0.001319446,0.001345897],"domain_scores_gemma":[0.990519,0.00453889,0.0001537293,0.002955979,0.001476587,0.0003558408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002516592,0.000759475,0.001751285,0.0001507495,0.0001058408,0.00001370473,0.0009580143,0.1457539,0.008442494,0.712392,0.03657846,0.09284236],"study_design_scores_gemma":[0.0005720638,0.0002077833,0.002006142,0.00003485037,0.000005089447,0.0000177063,0.00008213687,0.9619083,0.0003776429,0.02325206,0.01126427,0.0002719627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002506123,0.0003499044,0.9824862,0.00169966,0.000245584,0.001237894,0.00000764471,0.0002667792,0.01120018],"genre_scores_gemma":[0.6494502,0.00008383248,0.3493554,0.00007889313,0.0003116441,0.0003783724,0.0001599257,0.00002990856,0.0001518058],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8161544,"threshold_uncertainty_score":0.9998074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1917416443279943,"score_gpt":0.4616228872456383,"score_spread":0.2698812429176439,"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."}}