{"id":"W2803888420","doi":"10.23977/iotea.2017.31002","title":"The method of adaptive selection of a wireless access network in a heterogeneous environment based on the theory of fuzzy sets","year":2018,"lang":"en","type":"article","venue":"Internet of Things (IoT) and Engineering Applications","topic":"Information Systems and Technology Applications","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Wireless network; Handover; Node (physics); Fuzzy logic; Wireless; Vertical handover; Quality of service; Heterogeneous network; Heterogeneous wireless network; Adaptation (eye); Selection (genetic algorithm); Access network; Distributed computing; Computer network; Artificial intelligence; Engineering; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006145619,0.00008558061,0.0001472054,0.0001232529,0.00005097661,0.00001617634,0.0002829311,0.00006118934,0.000008272058],"category_scores_gemma":[0.00002056137,0.00006014918,0.00004075905,0.0002915772,0.0001153039,0.00007621949,0.00008582406,0.00009517149,0.000002090828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001432071,"about_ca_system_score_gemma":0.000006639565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000152528,"about_ca_topic_score_gemma":0.000009226205,"domain_scores_codex":[0.9993235,0.00001172534,0.0003665823,0.00009948083,0.0001024764,0.00009626179],"domain_scores_gemma":[0.9990909,0.0002310097,0.0003810419,0.0002164785,0.00007593146,0.000004665495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001110311,0.0001276324,0.001939512,0.0002884172,0.0001382119,7.220923e-8,0.0005654661,0.05533346,0.003472413,0.9054646,0.0003203284,0.03223883],"study_design_scores_gemma":[0.0003293307,0.00006374311,0.004152841,0.0002591031,0.00005865445,0.000002788243,0.0002992013,0.9562198,0.01662892,0.01067451,0.01114711,0.0001640062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.222397,0.00006835812,0.7729822,0.0003959159,0.00004482451,0.00089856,0.000006328099,0.00005258366,0.003154201],"genre_scores_gemma":[0.9975914,0.000004525874,0.00205812,0.00006430675,0.00003631384,0.0002194448,0.000002779598,0.000008665683,0.0000144602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9008864,"threshold_uncertainty_score":0.2452812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01054943588225633,"score_gpt":0.2188802266866113,"score_spread":0.208330790804355,"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."}}