{"id":"W2079511712","doi":"10.1145/1551950.1551961","title":"Towards comprehensive RRM frameworks for heterogeneous wireless networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Radio resource management; Standardization; Computer science; Status quo; Wireless; Wireless network; Resource management (computing); Heterogeneous network; Computer network; Resource (disambiguation); Risk analysis (engineering); Telecommunications; Business","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":[0.0001394319,0.0002696786,0.0003502086,0.00005717702,0.0002109222,0.0004078674,0.001135571,0.0004030474,0.00002666759],"category_scores_gemma":[0.000007068717,0.0002303214,0.0001982621,0.000326881,0.00003698495,0.0002978343,0.0001501292,0.000389306,0.000013308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003611535,"about_ca_system_score_gemma":0.00004926982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009096644,"about_ca_topic_score_gemma":0.000005930153,"domain_scores_codex":[0.9981802,0.00005429255,0.0003293459,0.0005636401,0.0002326313,0.0006399601],"domain_scores_gemma":[0.9985731,0.0001747496,0.0001092984,0.0007469979,0.0001970715,0.0001987354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004599374,0.00009958057,0.00001978577,0.00001273682,0.00003013149,0.00002145451,0.0001489931,0.05246303,0.00004630518,0.1429518,0.01109429,0.793066],"study_design_scores_gemma":[0.0005833266,0.0005222046,0.0003166195,0.00005327757,0.00000605335,0.00003059813,0.000009708751,0.9439863,0.0007110373,0.02416412,0.02920032,0.000416436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001342855,0.000245332,0.9892017,0.002654391,0.0004764508,0.004049278,0.000001706769,0.0004040331,0.001624275],"genre_scores_gemma":[0.8946672,0.00004036747,0.09260547,0.01042545,0.0006937465,0.001306165,0.000007679441,0.00002132767,0.0002325907],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8965962,"threshold_uncertainty_score":0.9392234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047696872988224,"score_gpt":0.2833882474090721,"score_spread":0.2629112786791899,"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."}}