{"id":"W2954066018","doi":"10.2316/j.2019.206-0233","title":"COGNITIVE RADIO RESOURCE ALLOCATION BASED ON THE IMPROVED QUANTUM GENETIC ALGORITHM","year":2019,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Satellite Communication Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Cognitive radio; Computer science; Genetic algorithm; Resource allocation; Quantum; Cognition; Algorithm; Computer network; Distributed computing; Psychology; Machine learning; Telecommunications; Neuroscience; Wireless; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003167344,0.00008165919,0.00009838531,0.0001374166,0.00002780133,0.00009348351,0.0002253218,0.00004148909,0.00001977627],"category_scores_gemma":[0.00006123678,0.0000624305,0.00004390092,0.00007023363,0.00001869234,0.0001044977,0.00001487127,0.0001388851,0.0000192859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007349773,"about_ca_system_score_gemma":0.000022411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002153533,"about_ca_topic_score_gemma":3.309142e-7,"domain_scores_codex":[0.9991724,0.00006565895,0.0003364015,0.00005886495,0.0003011297,0.00006547093],"domain_scores_gemma":[0.9990124,0.0003310681,0.0001993525,0.0001195926,0.0003074442,0.00003010309],"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":[0.000057693,0.00009197037,0.001518492,0.00004504826,0.0003332949,0.00000714894,0.0009849734,0.7863392,0.00671671,0.003018457,0.0003879042,0.2004991],"study_design_scores_gemma":[0.0004340844,0.00005595833,0.008974889,0.0001660169,0.00001193647,0.00003210267,0.0001578228,0.9880676,0.0006259953,0.0001483131,0.001256633,0.00006865718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2708614,0.001488462,0.7186429,0.004407484,0.002247062,0.0005583597,0.00001809616,0.0001210574,0.001655206],"genre_scores_gemma":[0.994938,0.0001431891,0.004587915,0.0001493278,0.000126585,0.000003012799,0.00001203523,0.00001493307,0.00002501112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7240766,"threshold_uncertainty_score":0.2545842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013070723177536,"score_gpt":0.2386266496825541,"score_spread":0.2255559265050181,"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."}}