{"id":"W2020196384","doi":"10.1109/icc.2012.6363842","title":"CM-MAC: A cognitive MAC protocol with mobility support in cognitive radio ad hoc networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer network; Computer science; Cognitive radio; Throughput; Wireless ad hoc network; Node (physics); Multiple Access with Collision Avoidance for Wireless; Protocol (science); Access control; Carrier sense multiple access with collision avoidance; Control channel; Media access control; Optimized Link State Routing Protocol; Wireless; Routing protocol; Telecommunications; Engineering; Base station; Network packet; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001001813,0.0004668579,0.0005338359,0.0002007592,0.0001973179,0.0002199174,0.0003814532,0.0001673749,0.0002825453],"category_scores_gemma":[0.0000834964,0.0003754365,0.0001235796,0.00116091,0.0002530168,0.00124658,0.0002736206,0.0006201058,0.00005646412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001644551,"about_ca_system_score_gemma":0.0001977777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004356014,"about_ca_topic_score_gemma":0.0004202409,"domain_scores_codex":[0.996465,0.0003095663,0.0005080054,0.0008878484,0.0004764493,0.001353092],"domain_scores_gemma":[0.9980207,0.0006602198,0.0001945836,0.0004016961,0.0003045061,0.0004182908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002264771,0.00303696,0.1471068,0.0000986396,0.0003445818,0.0007078599,0.006112987,0.0002368991,0.00004059903,0.004589452,0.001384147,0.8340763],"study_design_scores_gemma":[0.02629072,0.00537841,0.5706068,0.002136713,0.0002578008,0.001754993,0.003183092,0.370145,0.003440531,0.001463814,0.01019668,0.0051455],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05159281,0.000278157,0.8427715,0.0002947867,0.0002875734,0.06854855,0.00001262456,0.0004788027,0.03573522],"genre_scores_gemma":[0.9793126,0.00001902265,0.003783378,0.0008039948,0.0002714428,0.01534786,0.00001532123,0.00003715852,0.0004092133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9277198,"threshold_uncertainty_score":0.9998698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01907267115738762,"score_gpt":0.2802623521892383,"score_spread":0.2611896810318507,"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."}}