{"id":"W2020216268","doi":"10.1109/spawc.2014.6941869","title":"Distributed stochastic learning for dynamic spectrum access adaptive to primary network conditions","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Cognitive radio; Learning automata; Channel (broadcasting); Set (abstract data type); Collision; Distributed computing; Computer network; Adaptive learning; Selection (genetic algorithm); Automaton; Machine learning; Artificial intelligence; Computer security; Telecommunications; Wireless","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":[0.001807519,0.0001829168,0.0003663828,0.0002627226,0.0006030464,0.0004250303,0.001129409,0.00007054238,0.0006053883],"category_scores_gemma":[0.00525545,0.0001417144,0.0001185316,0.00145443,0.0001268032,0.0005284249,0.0004873834,0.0002817048,0.0004979769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002006701,"about_ca_system_score_gemma":0.0001053029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001356242,"about_ca_topic_score_gemma":0.0001860444,"domain_scores_codex":[0.9967772,0.000205168,0.0004726698,0.0006882884,0.00112108,0.0007356259],"domain_scores_gemma":[0.9929577,0.005681728,0.0001475732,0.000478047,0.0004298285,0.0003051782],"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":[0.0001036808,0.00002714907,0.0001305107,0.000002033123,0.00001529076,0.000001303438,0.00002946287,0.9801812,0.00002226025,0.001451309,0.009714493,0.008321262],"study_design_scores_gemma":[0.0005808657,0.000444339,0.02565141,0.00001827631,0.000009520103,0.000004810598,0.0001684001,0.8015791,0.00003579426,0.161715,0.00951699,0.0002755075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002421281,0.00001437335,0.9937456,0.001652898,0.0002963782,0.0008288505,0.0001072043,0.0001221628,0.0008113068],"genre_scores_gemma":[0.9864773,8.968144e-7,0.007330769,0.0002654803,0.0002703916,0.0001997264,0.0001548228,0.00002760573,0.005273002],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9864148,"threshold_uncertainty_score":0.6628577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08252314095925892,"score_gpt":0.4404324619688316,"score_spread":0.3579093210095726,"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."}}