{"id":"W2998910091","doi":"10.1109/tmc.2020.2965450","title":"Dynamic Model for Network Selection in Next Generation HetNets With Memory-Affecting Rational Users","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Alberta","funders":"National Research Foundation of Korea","keywords":"Computer science; Heterogeneous network; Evolutionary game theory; Game theory; Wireless network; Network formation; Selection (genetic algorithm); Service (business); Distributed computing; Wireless; Artificial intelligence; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001274963,0.00019858,0.0001969103,0.00009570264,0.0002326308,0.00005375737,0.00006329511,0.0000883867,0.0000046543],"category_scores_gemma":[0.000004557736,0.0002262305,0.00005271082,0.0004581632,0.0000113751,0.0003270523,7.447243e-7,0.0002392063,0.000003298529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002537464,"about_ca_system_score_gemma":0.00003278914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004490517,"about_ca_topic_score_gemma":0.0001949758,"domain_scores_codex":[0.9989436,0.00003531984,0.0003250897,0.0003048083,0.0001145311,0.0002766626],"domain_scores_gemma":[0.999622,0.0001046766,0.00006598522,0.00007536646,0.00006776409,0.00006421026],"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":[0.00002955561,0.00001724585,0.00001468354,0.00006061283,0.00002164728,4.511774e-7,0.0006365197,0.9794703,0.011749,0.000004101763,0.00002631784,0.007969567],"study_design_scores_gemma":[0.0006864009,0.0001189761,0.000007647607,0.00006495968,0.00001647875,0.000005981627,0.0001048304,0.9960175,0.00274452,0.000006330481,0.000005351874,0.000220993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1191962,0.00003590874,0.8791499,0.00001941337,0.0002602304,0.0009233999,0.000005409777,0.0003896673,0.00001985975],"genre_scores_gemma":[0.9051677,0.000006292715,0.09436394,0.00005276137,0.0001507628,0.0001591162,0.00001876822,0.00006700755,0.00001365172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7859715,"threshold_uncertainty_score":0.9225411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02307638037672945,"score_gpt":0.2381264948368271,"score_spread":0.2150501144600977,"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."}}