{"id":"W4391020482","doi":"10.1109/tnse.2024.3354792","title":"Distributed Learning of Unknown Games for HetNet Selection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Selection (genetic algorithm); Heterogeneous network; Computer science; A priori and a posteriori; Artificial intelligence; Wireless network; Wireless","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":[0.003843123,0.001941529,0.003334826,0.001077521,0.0011122,0.002384039,0.002857121,0.002785124,0.008556942],"category_scores_gemma":[0.01714883,0.001082711,0.001345649,0.0009817998,0.002619466,0.002681825,0.003242465,0.003764394,0.0009453141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002840447,"about_ca_system_score_gemma":0.002709205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007444545,"about_ca_topic_score_gemma":0.008847251,"domain_scores_codex":[0.9978341,0.0008842432,0.00009256838,0.0005560439,0.0002467299,0.00038636],"domain_scores_gemma":[0.981985,0.01512087,0.0008840953,0.0004807878,0.0008196418,0.0007096636],"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":[0.0001721462,0.00009933164,0.001111382,0.0001431586,0.00007559895,0.0001387468,0.0001258902,0.8974861,0.0002878906,0.08035082,0.003906329,0.01610257],"study_design_scores_gemma":[0.00003621674,0.00002112768,0.00008479132,0.00001084401,0.000009976296,0.00001175933,0.00001851387,0.9702823,0.00006151605,0.02896479,0.0004893377,0.000008977353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02321284,0.0007995746,0.9624897,0.001355785,0.0002050681,0.0001917753,0.0003134507,0.0003685647,0.01106322],"genre_scores_gemma":[0.8654294,0.001137399,0.105216,0.0009294476,0.000446556,0.0009237102,0.0008941352,0.0001813262,0.02484209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008556942,"threshold_uncertainty_score":0.02862579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007659775598852182,"score_gpt":0.2171167298615874,"score_spread":0.2094569542627352,"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."}}