{"id":"W2119381063","doi":"10.1109/wcnc.2007.697","title":"Application of ELECTRE to Network Selection in A Hetereogeneous Wireless Network Environment","year":2007,"lang":"en","type":"article","venue":"","topic":"IPv6, Mobility, Handover, Networks, Security","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"ELECTRE; Computer science; Ranking (information retrieval); Selection (genetic algorithm); Wireless network; Heterogeneous network; Access network; Selection algorithm; Service (business); Computer network; Distributed computing; Wireless; Multiple-criteria decision analysis; Artificial intelligence; Operations research; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005985203,0.0002016953,0.0002659081,0.00007265114,0.00003939535,0.00001061136,0.0001452912,0.0001638944,0.0000489911],"category_scores_gemma":[0.000003359154,0.0002283529,0.00005807998,0.0006097817,0.00001897101,0.00005592265,0.00004406258,0.0002125442,0.00003185599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000344232,"about_ca_system_score_gemma":0.000007969158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001231378,"about_ca_topic_score_gemma":0.002689758,"domain_scores_codex":[0.9983628,0.00003462465,0.0004686163,0.0002961518,0.000198049,0.0006397684],"domain_scores_gemma":[0.9994978,0.00007819884,0.00005040035,0.0002494646,0.00001805134,0.0001060826],"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":[0.00007544024,0.00006164299,0.01880889,0.00003330784,0.0000231999,0.000001807384,0.0001036993,0.942799,0.003651603,0.0003651834,0.0007256005,0.03335064],"study_design_scores_gemma":[0.0009570998,0.000303188,0.07435267,0.00006739204,0.00004488204,0.0000228086,0.00003467648,0.8809474,0.01826309,0.001715023,0.02241925,0.0008725398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5170106,0.0003921834,0.4801344,0.00001688325,0.0001812615,0.0007016016,0.000001310265,0.0001870768,0.001374693],"genre_scores_gemma":[0.9964584,0.00006927731,0.002668537,0.00009061874,0.0005413656,0.00007159398,0.00001204212,0.00004232225,0.00004592026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4794478,"threshold_uncertainty_score":0.931196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003116475873341729,"score_gpt":0.1880538258556869,"score_spread":0.1849373499823452,"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."}}