{"id":"W2616466037","doi":"","title":"A Tabu Search Heuristic for the Dimensioning of 3G Multi-Service Networks","year":2003,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Dimensioning; Tabu search; Heuristic; Computer science; A priori and a posteriori; Service (business); Mathematical optimization; Operations research; Algorithm; Artificial intelligence; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00146222,0.0009580188,0.001242044,0.001250371,0.0007577026,0.001137247,0.00128168,0.00131328,0.003026514],"category_scores_gemma":[0.003239852,0.0007275663,0.0008099824,0.001474952,0.0009984623,0.001302831,0.0006971452,0.0009834681,0.0003789689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001634225,"about_ca_system_score_gemma":0.001877488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007278957,"about_ca_topic_score_gemma":0.00560513,"domain_scores_codex":[0.9993119,0.000428365,0.00002086,0.000071504,0.00008044211,0.00008699037],"domain_scores_gemma":[0.9986078,0.001068881,0.0001041329,0.00005631204,0.0001064024,0.00005643936],"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":[0.00005116802,0.00003089014,0.0002149036,0.00004720697,0.0000250761,0.00002982384,0.00004462358,0.9739125,0.000207286,0.006799685,0.000972344,0.01766446],"study_design_scores_gemma":[0.00001950243,0.00003023362,0.00004839971,0.0000117898,0.000006668292,0.00001022376,0.00001611587,0.9951417,0.0001202277,0.004023351,0.0005661063,0.0000056062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0448918,0.001526098,0.9473187,0.0004149278,0.00009111594,0.0002022645,0.000205682,0.0006609418,0.004688449],"genre_scores_gemma":[0.4399309,0.0008028587,0.5554453,0.0002827252,0.00004789853,0.0005519486,0.0003640273,0.000180458,0.002393906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007278957,"threshold_uncertainty_score":0.01447314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220494165769431,"score_gpt":0.2350248554885643,"score_spread":0.21281991383087,"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."}}