{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004396642,0.000105968,0.0001182775,0.0001774771,0.0002734216,0.0002060995,0.0001374663,0.00003439948,0.000001474284],"category_scores_gemma":[0.00001089298,0.0001018658,0.0000496234,0.001574997,0.00006663895,0.0004149242,0.000002594668,0.000180008,6.990456e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005838142,"about_ca_system_score_gemma":0.00007969057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004697175,"about_ca_topic_score_gemma":0.0000035318,"domain_scores_codex":[0.9990298,0.000008745627,0.000139746,0.0003120318,0.000189709,0.0003200363],"domain_scores_gemma":[0.9995425,0.0001928503,0.00001882936,0.00008597878,0.00008797782,0.00007187933],"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":[0.0000035751,0.000007345147,0.000004004809,0.00002788022,0.00001277714,0.000001222638,0.0001147113,0.9033439,0.003696499,0.005671153,0.00005906553,0.08705784],"study_design_scores_gemma":[0.00007179964,0.0001149845,0.00007652242,0.0001410612,0.00001074356,0.00002223229,0.000006732828,0.9907559,0.005324447,0.0001560603,0.003205141,0.0001144336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01422326,0.0002921229,0.984208,0.0001155453,0.000783376,0.0001045182,0.000002033956,0.0002208609,0.00005028907],"genre_scores_gemma":[0.9904814,0.0001102252,0.009230397,0.00001354631,0.0001123157,0.00001013861,5.136706e-7,0.000008542926,0.00003296056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9762581,"threshold_uncertainty_score":0.4153967,"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."}}