{"id":"W2956441526","doi":"10.1109/icc.2019.8761211","title":"Dataset Modeling for Data-Driven AI-Based Personalized Wireless Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Personalization; Provisioning; Context (archaeology); Wireless network; Computer user satisfaction; User satisfaction; Wireless; Distributed computing; Computer network; User experience design; Human–computer interaction; World Wide Web; User interface design; Telecommunications","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":[0.0005056825,0.0001980154,0.0002859166,0.00004821393,0.000129241,0.0003408696,0.002589918,0.0001069646,0.00009977086],"category_scores_gemma":[0.000007098978,0.0001666897,0.00006809045,0.0002125845,0.00002376211,0.0008227427,0.0005857618,0.0001673191,0.00005626259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002178883,"about_ca_system_score_gemma":0.0001255443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005817066,"about_ca_topic_score_gemma":0.00002555749,"domain_scores_codex":[0.9981596,0.00006275313,0.0002799692,0.0007778363,0.0002513476,0.0004684184],"domain_scores_gemma":[0.9975113,0.0001956739,0.00008067636,0.002006326,0.00008263128,0.0001234505],"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":[0.00007151928,0.0000585893,0.0002364411,0.00005299197,0.00002901245,0.000002864634,0.00002340763,0.8811892,0.00002762248,0.0266331,0.07471891,0.0169564],"study_design_scores_gemma":[0.001174946,0.00006630165,0.00000247372,0.00004321151,0.000006773158,0.000001497514,0.000004116388,0.9404162,0.00001542224,0.0002744866,0.05774375,0.0002508269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003587419,0.00003932903,0.9929417,0.0009128352,0.000261297,0.004823663,0.0003895321,0.0001381963,0.0001346787],"genre_scores_gemma":[0.5664085,0.00001747435,0.3967896,0.01418342,0.0008673224,0.004390292,0.01647598,0.00009318016,0.0007742835],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5961521,"threshold_uncertainty_score":0.6797409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05566828420395405,"score_gpt":0.3167033556421913,"score_spread":0.2610350714382373,"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."}}