{"id":"W2035083350","doi":"10.1109/glocom.2010.5683347","title":"Cognitive MAC Protocol with Transmission Tax: Dynamically Adjusting Sensing and Data Performance","year":2010,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Federation for the Humanities and Social Sciences","keywords":"Computer science; Transmission (telecommunications); Data transmission; Computer network; Node (physics); Protocol (science); Cognition; Process (computing); Real-time computing; Telecommunications; Engineering; Neuroscience; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003223117,0.0001765263,0.0001611507,0.00005946506,0.0002878325,0.0002553952,0.0003382136,0.00006099173,0.00001416079],"category_scores_gemma":[0.00003010196,0.0001271872,0.00001396671,0.0002511906,0.0001091574,0.0007279514,0.0002810531,0.0003986887,0.000003379563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008076366,"about_ca_system_score_gemma":0.00007332497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004052186,"about_ca_topic_score_gemma":0.000243014,"domain_scores_codex":[0.9985936,0.00003801161,0.0001773615,0.0006315437,0.0002286557,0.0003307996],"domain_scores_gemma":[0.9990723,0.0001638389,0.00007259582,0.000432074,0.0001175467,0.0001416454],"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":[0.00004738049,0.00001990159,0.0006522328,0.00003171657,0.00001056913,0.00003623504,0.0001421423,0.000006615462,0.003203707,0.0003294751,0.00002060092,0.9954994],"study_design_scores_gemma":[0.0007775758,0.0001392391,0.005364227,0.0002408278,0.00001268476,0.0003860591,0.00003430036,0.9904051,0.001551938,0.00006990093,0.0007673219,0.0002508482],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0942853,0.000003434456,0.8905518,0.0003605422,0.00004597995,0.009033414,0.000002537576,0.0001607586,0.005556242],"genre_scores_gemma":[0.8315294,0.000002720297,0.1679458,0.0001718044,0.00009451793,0.0001443105,0.000006731309,0.00001429091,0.00009037441],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9952486,"threshold_uncertainty_score":0.5186544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712761652877231,"score_gpt":0.2721190270019777,"score_spread":0.2549914104732054,"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."}}