{"id":"W2100597260","doi":"10.1002/wcm.2585","title":"Cluster‐based coordination scheme for cooperative cognitive radio networks","year":2015,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Computer science; Computer network; Cluster (spacecraft); Scheme (mathematics); Throughput; Cognitive network; Selection (genetic algorithm); Matching (statistics); Bipartite graph; Transmission (telecommunications); Linear network coding; Reliability (semiconductor); Distributed computing; Telecommunications; Theoretical computer science; Artificial intelligence; Wireless","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009125668,0.0005180597,0.0005805611,0.0006221721,0.0008475747,0.0006647953,0.001813101,0.0005716893,0.001002714],"category_scores_gemma":[0.001590463,0.000215029,0.0003138659,0.0008057258,0.0006069946,0.0006256197,0.001208823,0.0005156332,0.0002040502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001778,"about_ca_system_score_gemma":0.001063706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004150948,"about_ca_topic_score_gemma":0.002862956,"domain_scores_codex":[0.9990547,0.0002829549,0.00003611651,0.0002212242,0.0002560779,0.0001489065],"domain_scores_gemma":[0.999218,0.0002309982,0.0001129277,0.0001202381,0.0002309471,0.00008683193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004376231,0.0001408139,0.0008993207,0.00009549006,0.0001185919,0.0002342528,0.0002986376,0.7927458,0.01783524,0.06732929,0.005503974,0.114361],"study_design_scores_gemma":[0.00003140138,0.00006627398,0.0001626832,0.000003500504,0.00001523273,0.00006000365,0.00001729229,0.990715,0.001653985,0.005805114,0.001454537,0.00001504373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03457083,0.0002772868,0.9597768,0.0001331408,0.00007250096,0.00009956484,0.00003010027,0.0003289567,0.004710737],"genre_scores_gemma":[0.9481177,0.0001113182,0.04945752,0.00006754445,0.00003057216,0.0001247996,0.00004051088,0.00001677007,0.002033338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004150948,"threshold_uncertainty_score":0.008253574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05756278858158403,"score_gpt":0.3204622406239481,"score_spread":0.2628994520423641,"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."}}