{"id":"W2041488232","doi":"10.1109/jproc.2009.2015718","title":"Robust Transmit Power Control for Cognitive Radio","year":2009,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":200,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Cognitive radio; Computer science; Transmitter power output; Power control; Interference (communication); Wireless; Wireless network; Computer network; Radio resource management; Resource (disambiguation); Control (management); Resource allocation; Focus (optics); Power (physics); Distributed computing; Transmitter; Telecommunications; Artificial intelligence; Channel (broadcasting)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002595996,0.0001398139,0.0002240866,0.00005189874,0.0001496636,0.00009980408,0.0005251425,0.00005267769,0.000002255229],"category_scores_gemma":[0.00007036196,0.0001019764,0.0001915388,0.0002766787,0.00005948436,0.000306953,0.00001500757,0.0001239523,0.000001320959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002490085,"about_ca_system_score_gemma":0.00002528841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001716193,"about_ca_topic_score_gemma":7.202673e-7,"domain_scores_codex":[0.9990059,0.000005211994,0.0002011283,0.0002821894,0.0002063885,0.0002991649],"domain_scores_gemma":[0.9992411,0.0001324837,0.000142611,0.00008058698,0.0003466913,0.00005652699],"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":[0.001686276,0.00124957,0.00258626,0.0002479384,0.0008189426,0.00001184069,0.01444419,0.000985971,0.2046087,0.3493068,0.03400896,0.3900445],"study_design_scores_gemma":[0.01596371,0.003183826,0.04092954,0.001635274,0.0005264933,0.0003176749,0.0007641236,0.4437855,0.3617614,0.1260512,0.003043784,0.002037499],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2710206,0.0003830346,0.7062676,0.008484074,0.0007671813,0.001343843,0.00001410736,0.0001754121,0.0115442],"genre_scores_gemma":[0.9950814,0.000008435011,0.003755817,0.000876212,0.0001509861,0.000004209846,1.225438e-7,0.000008498093,0.0001142913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7240608,"threshold_uncertainty_score":0.4158477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508372111800191,"score_gpt":0.219644127945626,"score_spread":0.2045604068276241,"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."}}