{"id":"W2168297205","doi":"10.1109/glocom.2009.5425232","title":"Simple and Efficient MAC for Cognitive Wireless Personal Area Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer network; Computer science; Network packet; Probabilistic logic; Node (physics); Simple (philosophy); Wireless; Real-time computing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001548194,0.001244731,0.001157429,0.0006571944,0.0007404209,0.001245838,0.001698329,0.0009608251,0.001303946],"category_scores_gemma":[0.003401298,0.0004828628,0.0006843895,0.0006427872,0.0009619325,0.001841499,0.001217803,0.001133857,0.000392593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007247815,"about_ca_system_score_gemma":0.00117539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001325183,"about_ca_topic_score_gemma":0.001786256,"domain_scores_codex":[0.9983522,0.0004153989,0.00004598061,0.0001682058,0.0008373269,0.00018096],"domain_scores_gemma":[0.9985471,0.0006240389,0.0001873808,0.0003838796,0.0002077284,0.00004984235],"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":[0.0002453507,0.0003527694,0.0007065713,0.0004392363,0.0002023612,0.0002617856,0.0001366169,0.7658735,0.03989963,0.1014189,0.002203132,0.08826013],"study_design_scores_gemma":[0.00001966505,0.0001160464,0.000240209,0.000007449162,0.00003046471,0.0001032415,0.0000130435,0.9794996,0.00227965,0.01600573,0.001662557,0.00002231476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04185463,0.001396718,0.9513483,0.0001392569,0.000150785,0.0001748738,0.00005512769,0.000464318,0.004415947],"genre_scores_gemma":[0.7598158,0.0009661506,0.2341117,0.0001431396,0.0001751215,0.0002555623,0.00007257329,0.00007986908,0.004380185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001698329,"threshold_uncertainty_score":0.008187771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01369636446356114,"score_gpt":0.2410233639291535,"score_spread":0.2273269994655923,"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."}}