{"id":"W2125955963","doi":"10.1109/tvt.2007.912601","title":"On the Design of Large-Scale UMTS Mobile Networks Using Hybrid Genetic Algorithms","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"UMTS frequency bands; Roaming; Computer science; Cellular network; Computer network; Node (physics); Mobile telephony; Heuristic; Algorithm; Mobile computing; Distributed computing; Mobile radio; Engineering; Artificial intelligence","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":[0.0009851645,0.0007145324,0.0006078355,0.0006699118,0.0004395391,0.0007513519,0.0008263584,0.000718986,0.001052544],"category_scores_gemma":[0.002564496,0.0004000707,0.0005349475,0.000574442,0.0007365647,0.0004602941,0.0006257366,0.0004167143,0.0001306748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008133015,"about_ca_system_score_gemma":0.001032086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005359515,"about_ca_topic_score_gemma":0.005406769,"domain_scores_codex":[0.9996998,0.0001374767,0.000009984919,0.00004294647,0.00006628312,0.00004348579],"domain_scores_gemma":[0.9990839,0.0006592701,0.0001023557,0.0000360365,0.00008713531,0.00003131828],"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":[0.00001060312,0.000008692869,0.0001497347,0.00000901884,0.000007799486,0.00001581869,0.00001401739,0.9901355,0.0004112203,0.001595697,0.00006424403,0.007577707],"study_design_scores_gemma":[0.000006989934,0.00001888307,0.0000421029,0.000003430882,0.000004615334,0.000005771649,0.000008318113,0.9986657,0.0001946605,0.0008604015,0.0001875384,0.000001681282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06592461,0.0003250344,0.9284973,0.0001630827,0.00002490147,0.00009890948,0.00002366158,0.0002624754,0.004680026],"genre_scores_gemma":[0.6555702,0.0003335121,0.3417855,0.0001110541,0.00001893021,0.0003158384,0.00005322671,0.00005285684,0.00175896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005359515,"threshold_uncertainty_score":0.0106566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03334866353553348,"score_gpt":0.2599238704159909,"score_spread":0.2265752068804574,"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."}}