{"id":"W1968093411","doi":"10.4028/www.scientific.net/amm.48-49.314","title":"Cognitive Radio Decision Engine Based on Multi-Objective Genetic Algorithm","year":2011,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cognitive radio; Weighting; Genetic algorithm; Computer science; Mathematical optimization; Population; Adaptation (eye); Multi-objective optimization; Optimization problem; Algorithm; Machine learning; Mathematics; Wireless; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002810492,0.0002963963,0.0003290244,0.0001865885,0.0001554075,0.00009493742,0.0003070422,0.0001238769,0.00005950145],"category_scores_gemma":[0.00006055614,0.0002749651,0.00003503713,0.0002433887,0.00002379969,0.0001553554,0.0001919184,0.00009208134,0.00005294888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005030892,"about_ca_system_score_gemma":0.00004676804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001522488,"about_ca_topic_score_gemma":0.000001028418,"domain_scores_codex":[0.9983587,0.00005495398,0.0003094678,0.0007070304,0.0002388737,0.0003309637],"domain_scores_gemma":[0.999,0.0001551402,0.0001678366,0.0003711524,0.000156106,0.0001497828],"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":[0.0003906056,0.0008713815,0.000001853774,0.00003759886,0.0001071142,0.0001032607,0.002186547,0.001308668,0.017587,0.1755833,0.00003159232,0.801791],"study_design_scores_gemma":[0.005010727,0.0004894141,0.0004950688,0.00008585961,0.00004321086,0.00002572307,0.0001447562,0.6213558,0.3274651,0.04398004,0.00008996949,0.0008143597],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007253884,0.00002788435,0.9974472,0.000007906285,0.0005871085,0.0007319864,0.00005223599,0.0001779031,0.0002424002],"genre_scores_gemma":[0.1249053,0.00005801564,0.8744224,0.0003523609,0.00004718544,0.0001499076,0.0000120666,0.00003536475,0.00001740573],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8009767,"threshold_uncertainty_score":0.9999703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01837230258933343,"score_gpt":0.2403007173975722,"score_spread":0.2219284148082388,"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."}}