{"id":"W1971951421","doi":"10.1109/wcsp.2012.6542950","title":"Network selection strategy for cognitive radios with heterogeneous primary networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cognitive radio; Computer science; Selection (genetic algorithm); Cognitive network; Markov chain; Greedy algorithm; Computer network; Service (business); Network performance; Distributed computing; Machine learning; Telecommunications; Algorithm; Wireless","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":[0.0003165123,0.0002405583,0.0002564261,0.00005209348,0.0003277631,0.0001901878,0.0001831072,0.00009528359,0.00001547248],"category_scores_gemma":[0.000007878972,0.0001959105,0.00008322037,0.0004849298,0.00004668494,0.0005966912,0.00005807252,0.0001730368,0.000006517306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008272004,"about_ca_system_score_gemma":0.00005966951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001047831,"about_ca_topic_score_gemma":0.00004424067,"domain_scores_codex":[0.998197,0.00008278019,0.0002063569,0.0003959787,0.0001801988,0.0009377083],"domain_scores_gemma":[0.9990259,0.0003420108,0.0001026762,0.0001679066,0.00016013,0.0002014107],"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":[0.0005894222,0.0003837366,0.01274151,0.00003962815,0.0004868991,0.00003181534,0.0003919579,0.3234795,0.00007510188,0.04941433,0.005785962,0.6065802],"study_design_scores_gemma":[0.0015394,0.001009002,0.008963465,0.0000945119,0.000090894,0.0007735746,0.00003195609,0.9838039,0.0004275156,0.001073619,0.001430458,0.0007617093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009569362,0.0009682649,0.9829856,0.00008007376,0.0003429401,0.0005788392,0.000001076185,0.00027072,0.005203189],"genre_scores_gemma":[0.9713463,0.00002832517,0.0254651,0.001142582,0.001741714,0.00003804455,0.00001523255,0.00002838337,0.0001943051],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.961777,"threshold_uncertainty_score":0.7988998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865293412812885,"score_gpt":0.235855247960264,"score_spread":0.2172023138321351,"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."}}