{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004917126,0.0001502116,0.0002209807,0.00005736039,0.00008151996,0.0001268858,0.0007030198,0.0001261766,0.0000252548],"category_scores_gemma":[0.000002974236,0.0001238742,0.0001309531,0.0001715423,0.00001828038,0.0002435009,0.0001330191,0.0001776738,0.00001796137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001526049,"about_ca_system_score_gemma":0.00003775939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007818562,"about_ca_topic_score_gemma":9.146142e-7,"domain_scores_codex":[0.9986687,0.00006854514,0.0002075497,0.0004892579,0.000152348,0.0004135655],"domain_scores_gemma":[0.9989927,0.0001717769,0.00004312078,0.0006009179,0.00004987232,0.0001415805],"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":[0.000007334394,0.00002248679,0.00002040254,0.00001404793,0.000009388505,0.000001990853,0.00008724398,0.01736768,0.0004265652,0.9019089,0.0003011891,0.07983278],"study_design_scores_gemma":[0.0002408761,0.00004905064,0.000001738495,0.00001022185,0.000003272509,0.000002583481,0.000001676486,0.8875582,0.001010489,0.1093327,0.001617782,0.0001714256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001117997,0.00008769824,0.9909514,0.0005394284,0.000494953,0.000378524,6.265777e-7,0.000157015,0.006272424],"genre_scores_gemma":[0.3389571,0.00000645914,0.6588332,0.0004664333,0.0001142672,0.000014579,0.000001773534,0.00001237301,0.001593831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8701905,"threshold_uncertainty_score":0.5051441,"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."}}