{"id":"W2968392233","doi":"10.1145/3341216.3342217","title":"Hierarchical Bayesian Modelling for Wireless Cellular Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Pooling; Computer science; Cellular network; Bayesian network; Wireless network; Bayesian probability; Data mining; Population; Parametric statistics; Parametric model; Machine learning; Wireless; Artificial intelligence; Computer network; Telecommunications; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007638071,0.001491538,0.002087475,0.002711226,0.001081356,0.002524471,0.003836195,0.002692724,0.003760644],"category_scores_gemma":[0.02359267,0.001351208,0.002248444,0.003955593,0.001430961,0.002939001,0.001911082,0.003284371,0.001191501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003258537,"about_ca_system_score_gemma":0.001855937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04664634,"about_ca_topic_score_gemma":0.04995306,"domain_scores_codex":[0.9959705,0.002367862,0.0001827204,0.0005774941,0.0006872562,0.0002142359],"domain_scores_gemma":[0.9887053,0.009322003,0.0006041122,0.0004428872,0.0007608967,0.0001647563],"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":[0.00006467694,0.00003924913,0.001576682,0.000119184,0.0001163157,0.00005789923,0.0001280307,0.9018719,0.0002717488,0.05756257,0.003695638,0.03449612],"study_design_scores_gemma":[0.00001073234,0.0000082652,0.0002574155,0.00001752592,0.00001165818,0.00001290464,0.00001165326,0.9550836,0.00005892212,0.04327746,0.00123916,0.00001067555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005430378,0.001021399,0.9904996,0.0005725724,0.00004757974,0.00008394938,0.0008828409,0.0003650353,0.001096682],"genre_scores_gemma":[0.4066917,0.00386098,0.5699296,0.0006040487,0.0004981392,0.001349959,0.008708066,0.0003047456,0.008052835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04664634,"threshold_uncertainty_score":0.09274971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01577323246974653,"score_gpt":0.2411631139019269,"score_spread":0.2253898814321804,"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."}}