{"id":"W2170536788","doi":"10.1109/cwit.2011.5872159","title":"Cross-layer resource allocation approach for multi-hop distributed cognitive radio network","year":2011,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer network; Network packet; Cognitive radio; Resource allocation; Hop (telecommunications); Node (physics); Network layer; Distributed computing; Wireless; Layer (electronics); Telecommunications; Engineering","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":[0.0004964697,0.0002332307,0.0002476977,0.0000565985,0.0003847565,0.0002346843,0.000431671,0.0001187414,0.00002468939],"category_scores_gemma":[0.00009258906,0.0002118898,0.0001400122,0.0005020979,0.0001075835,0.0003950318,0.0001488307,0.0001638803,0.00001262999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005703294,"about_ca_system_score_gemma":0.00004455466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000435796,"about_ca_topic_score_gemma":0.000020137,"domain_scores_codex":[0.9981212,0.00009234875,0.0003122248,0.000673315,0.0001856444,0.0006152604],"domain_scores_gemma":[0.9988481,0.0002161331,0.0001290834,0.0003549515,0.0002971423,0.0001545996],"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":[0.001350296,0.002797988,0.02952498,0.0001584954,0.001114407,0.00009871321,0.01140535,0.01281028,0.0006053229,0.3732053,0.02432886,0.5426],"study_design_scores_gemma":[0.001432136,0.0001238532,0.01865678,0.00003018492,0.00002920296,0.00002921457,0.0001166542,0.9755753,0.001245212,0.0006530012,0.001705552,0.0004028748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003909952,0.0001589111,0.9854941,0.00006317058,0.0001750181,0.0006456185,0.0000133147,0.0003023962,0.009237473],"genre_scores_gemma":[0.7195315,0.000007274106,0.2790934,0.0003438331,0.0003073137,0.00004287634,0.0001108636,0.00002059368,0.0005424034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.962765,"threshold_uncertainty_score":0.8640616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06191012891596148,"score_gpt":0.2816413163064562,"score_spread":0.2197311873904947,"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."}}