{"id":"W2490558322","doi":"10.1002/wcm.2713","title":"Adaptive channel selection and slot length configuration in cognitive radio","year":2016,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Channel (broadcasting); Cognitive radio; Idle; Selection (genetic algorithm); Markov process; Process (computing); Markov chain; Channel state information; Algorithm; Computer network; Real-time computing; Telecommunications; Artificial intelligence; Machine learning; Mathematics; Wireless; 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":[0.0003370763,0.0001401071,0.0001907148,0.0001443273,0.0004092393,0.0001163014,0.0002519142,0.0000582505,0.00000136505],"category_scores_gemma":[0.00002404237,0.0001195832,0.0000234907,0.0003193087,0.0001413314,0.0003496698,0.0003524654,0.000166212,0.000001620626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005633305,"about_ca_system_score_gemma":0.00003693479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008384494,"about_ca_topic_score_gemma":0.0002054111,"domain_scores_codex":[0.9988785,0.0002064044,0.0002494437,0.0003427875,0.00008804003,0.0002348032],"domain_scores_gemma":[0.9985794,0.0007907544,0.0001206586,0.0003109251,0.0001306879,0.00006750711],"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":[0.00001004705,0.00005867529,0.0009009438,0.000004429531,0.0000180035,0.000001836743,0.001870773,0.00004403919,0.00131258,0.0231032,0.000005389713,0.9726701],"study_design_scores_gemma":[0.0009189597,0.0001759094,0.01212496,0.0004135925,0.000008795471,0.00005909763,0.0005005428,0.9839692,0.0005721203,0.000812698,0.0002006923,0.0002434369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4109499,0.001170865,0.5862482,0.0005231503,0.00005109638,0.0003123628,0.000002093196,0.00008725438,0.0006550701],"genre_scores_gemma":[0.9949871,0.001576806,0.003277104,0.0000595547,0.00004424712,0.00002421562,0.00000328073,0.000009688197,0.00001803348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9839252,"threshold_uncertainty_score":0.487646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257453399077453,"score_gpt":0.2653454458859417,"score_spread":0.2396001059781964,"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."}}