{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002042159,0.0009345195,0.0006908578,0.0009199738,0.000857508,0.001404181,0.001494819,0.0007494518,0.0004175466],"category_scores_gemma":[0.009001425,0.0002506155,0.0002283381,0.001105179,0.001620712,0.001337068,0.001288138,0.0009207675,0.0001304501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009963006,"about_ca_system_score_gemma":0.002053743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003186051,"about_ca_topic_score_gemma":0.002979143,"domain_scores_codex":[0.9989017,0.0001851671,0.00005361747,0.0001652492,0.0004012656,0.0002929608],"domain_scores_gemma":[0.9964256,0.001376618,0.000648996,0.0006858124,0.0006829434,0.0001800858],"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":[0.00196806,0.0006795826,0.007567034,0.0003713806,0.0002769238,0.001164769,0.001047968,0.4715669,0.1360446,0.1601957,0.005166707,0.2139503],"study_design_scores_gemma":[0.0001015039,0.0004454053,0.001966776,0.00002284882,0.0001232552,0.0007051982,0.00009114297,0.9574813,0.01346712,0.02310533,0.002397892,0.00009220308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3003944,0.00108624,0.6802136,0.000751378,0.0002610904,0.0002956026,0.0001176351,0.001231806,0.01564823],"genre_scores_gemma":[0.9848402,0.0001631508,0.01395625,0.00009754339,0.00005307385,0.00008654995,0.00001935751,0.00001972853,0.0007641891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003186051,"threshold_uncertainty_score":0.01080012,"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."}}