{"id":"W2518829358","doi":"10.1002/nav.21701","title":"Column generation for stochastic green telecommunication network planning with switchable base stations","year":2016,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Column generation; Base station; Mathematical optimization; Computer science; Column (typography); Base (topology); Energy consumption; Reduction (mathematics); Telecommunications network; Network planning and design; Scheme (mathematics); Operations research; Telecommunications; Mathematics; Engineering; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095148,0.001055671,0.001258683,0.0006221181,0.0003798192,0.001177454,0.0008376613,0.0008309361,0.004600231],"category_scores_gemma":[0.002021324,0.0006748642,0.0008689877,0.001341674,0.0006965884,0.0006798342,0.0007734951,0.001217841,0.0002554797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001398173,"about_ca_system_score_gemma":0.001373426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01058022,"about_ca_topic_score_gemma":0.009302565,"domain_scores_codex":[0.9994619,0.0002447651,0.00001452738,0.00007370293,0.00009844339,0.0001066007],"domain_scores_gemma":[0.9984907,0.001141142,0.0001339427,0.00004778188,0.0001118428,0.00007457723],"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":[0.0000240984,0.00001211249,0.0001029026,0.00001928365,0.000009972686,0.00003309239,0.000008046821,0.9931597,0.0001409974,0.003358508,0.0003531174,0.002778287],"study_design_scores_gemma":[0.000008425352,0.0000102942,0.00003842529,0.000002716837,0.000003286994,0.000004150686,0.000005883117,0.9973238,0.0001083048,0.00231418,0.0001781892,0.000002416133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09505977,0.0005709033,0.8935488,0.0005803469,0.00009426592,0.0001834867,0.0009390382,0.000345439,0.008677892],"genre_scores_gemma":[0.8805721,0.0003938922,0.1134902,0.0001636874,0.00004490518,0.0003012875,0.0007841217,0.00009908026,0.004150675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01058022,"threshold_uncertainty_score":0.02103728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2023763461712704,"score_gpt":0.401718522681826,"score_spread":0.1993421765105556,"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."}}