{"id":"W2051287757","doi":"10.1109/chinacom.2008.4685018","title":"Memetic algorithms with multi-local-search for resource allocation in multiuser OFDM based Cognitive Radio systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Orthogonal frequency-division multiplexing; Cognitive radio; Memetic algorithm; Computer science; Wireless; Flexibility (engineering); Resource allocation; Multiplexing; Spectral efficiency; Algorithm; Computer network; Local search (optimization); Channel (broadcasting); Telecommunications; Mathematics","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":[0.0001609328,0.0001913675,0.0002271669,0.0001863808,0.00007517546,0.00001536853,0.00009009514,0.00009986464,0.000008208722],"category_scores_gemma":[0.00002392147,0.0001777556,0.00002836987,0.000428125,0.00007397553,0.0001626009,0.000009176734,0.0001438523,0.0000082862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001840458,"about_ca_system_score_gemma":0.00003289292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006188233,"about_ca_topic_score_gemma":0.00006974172,"domain_scores_codex":[0.9988632,0.00005063306,0.00027179,0.0002666202,0.0002061025,0.0003417004],"domain_scores_gemma":[0.9992807,0.0002808737,0.00003151018,0.000164958,0.0001636938,0.00007825752],"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":[0.00006670428,0.0000489507,0.0005424594,0.00008143736,0.00002099553,0.000007604116,0.0002927347,0.9956612,0.0001136621,0.00002597467,0.0000643163,0.003073991],"study_design_scores_gemma":[0.002967372,0.00006760415,0.0007904555,0.0001481172,0.0000113906,0.000007721249,0.0005032668,0.9918078,0.003279232,4.735785e-7,0.0001722146,0.0002443853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01578872,0.0002404262,0.9821455,0.00001867483,0.00005904185,0.001212375,0.000009910518,0.0003038852,0.0002214839],"genre_scores_gemma":[0.8958642,0.00003925008,0.1031778,0.00002545295,0.00005076602,0.0003732066,0.0001213026,0.00008325565,0.0002646755],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8800755,"threshold_uncertainty_score":0.7248662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02537000430916121,"score_gpt":0.2443725950185302,"score_spread":0.219002590709369,"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."}}