{"id":"W2055490185","doi":"10.1109/mnet.2007.314536","title":"Automated network selection in a heterogeneous wireless network environment","year":2007,"lang":"en","type":"article","venue":"IEEE Network","topic":"IPv6, Mobility, Handover, Networks, Security","field":"Engineering","cited_by":256,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer network; Heterogeneous network; Wireless network; Terminal (telecommunication); Heterogeneous wireless network; Wireless WAN; Selection (genetic algorithm); Network management station; Distributed computing; Process (computing); Service (business); Access network; Wireless; Network architecture; Wi-Fi array; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001747627,0.0006641607,0.0007265034,0.00009991697,0.0002584623,0.00008546382,0.0003975451,0.0006081405,0.00008290284],"category_scores_gemma":[0.000006948945,0.0007784053,0.0002037761,0.001308232,0.0001057279,0.0001845305,0.00009295955,0.0009272053,0.0001582233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008355016,"about_ca_system_score_gemma":0.00002948782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006919081,"about_ca_topic_score_gemma":0.002051691,"domain_scores_codex":[0.9950477,0.0002092274,0.001051966,0.0007345949,0.0004964305,0.002460006],"domain_scores_gemma":[0.9986358,0.000305656,0.0001575448,0.0005485947,0.00003391863,0.0003184412],"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":[0.0001820911,0.00008124205,0.01159938,0.00004665282,0.0001066772,0.00009055993,0.0001159706,0.9514859,0.0001039656,0.00004075002,0.03283247,0.003314398],"study_design_scores_gemma":[0.001336967,0.0001630049,0.02449765,0.0002407828,0.00008281249,0.00009217108,0.00001008125,0.9361787,0.0003622884,0.001653606,0.03418902,0.001192845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9363468,0.006706854,0.03010825,0.00002550572,0.01800123,0.001681278,0.000007569065,0.004470728,0.002651746],"genre_scores_gemma":[0.9812527,0.0004317683,0.001236951,0.0002203203,0.01645791,0.0001013608,0.000031521,0.0001932234,0.00007430196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0449058,"threshold_uncertainty_score":0.9994667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006414803904344322,"score_gpt":0.2046742232474834,"score_spread":0.1982594193431391,"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."}}