{"id":"W3130014465","doi":"10.1109/vtc2020-fall49728.2020.9348836","title":"User Persona in Personalized Wireless Networks: A Big Data-Driven Prediction Framework","year":2020,"lang":"en","type":"article","venue":"","topic":"Persona Design and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Personalization; Computer science; Persona; User satisfaction; Wireless network; Big data; Wireless; Process (computing); Human–computer interaction; World Wide Web; Data mining","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":[0.0001476483,0.0001417123,0.0001723499,0.00005243025,0.00009746491,0.0001576598,0.001515138,0.000107117,0.00004594611],"category_scores_gemma":[0.00003550582,0.0001319898,0.00004396373,0.0009116858,0.00005234502,0.0004674744,0.0003932251,0.000278879,0.00008605245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003559108,"about_ca_system_score_gemma":0.00007469489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004575764,"about_ca_topic_score_gemma":0.00001942199,"domain_scores_codex":[0.9985326,0.00007010148,0.0002019745,0.0006678105,0.0002496188,0.0002778855],"domain_scores_gemma":[0.9989132,0.0001097739,0.00005067976,0.0007153481,0.00003178462,0.0001791628],"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":[0.0001011899,0.0006043009,0.0268521,0.00007263606,0.0001414653,0.00008390077,0.01988243,0.004395043,0.002946268,0.6416621,0.123202,0.1800566],"study_design_scores_gemma":[0.000323965,0.00002892752,0.002355306,0.00002241332,0.000005006224,0.000003934107,0.0002032346,0.9765056,0.00001221521,0.0003453614,0.02003958,0.0001544789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005461547,0.0001338465,0.9845244,0.008372864,0.0001442117,0.0002394795,0.00002113776,0.0003403645,0.0007621989],"genre_scores_gemma":[0.9322984,0.00009114575,0.06182921,0.004880508,0.0005260013,0.00006233986,0.00005197907,0.00001478413,0.0002456283],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9721105,"threshold_uncertainty_score":0.5382386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07465955896278235,"score_gpt":0.2723890690752836,"score_spread":0.1977295101125012,"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."}}