{"id":"W2096347762","doi":"10.1186/1687-1499-2011-36","title":"Profit optimization in multi-service cognitive mesh network using machine learning","year":2011,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Quality of service; Reinforcement learning; Cognitive radio; Computer network; Profit (economics); Artificial intelligence; 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":[0.001020299,0.0002430474,0.0003076354,0.0002161118,0.001198601,0.0003060033,0.0007333754,0.00009195635,0.000008083834],"category_scores_gemma":[0.00002175692,0.0002384135,0.00006478429,0.001070696,0.00007885075,0.0005133966,0.0004815056,0.001147461,0.000002221057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008512365,"about_ca_system_score_gemma":0.00005582799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009043343,"about_ca_topic_score_gemma":0.0003453679,"domain_scores_codex":[0.9977336,0.0007530303,0.0005148063,0.0003184978,0.0001948722,0.0004852059],"domain_scores_gemma":[0.9983243,0.0004528318,0.0004198779,0.000450839,0.0002038775,0.0001483086],"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":[0.0002421937,0.0005319766,0.2251254,0.0000311536,0.0002627299,0.0002431146,0.0116235,0.2219391,0.0001360776,0.01267639,0.00001339406,0.5271749],"study_design_scores_gemma":[0.0007468841,0.00009305523,0.004108118,0.0008494061,0.0000224308,0.0002394791,0.0001346555,0.9931201,0.00001392458,0.0002250522,0.0001707452,0.0002761816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09844044,0.006625451,0.8920968,0.0004808108,0.0004180152,0.0003328751,9.258731e-7,0.0001151101,0.0014896],"genre_scores_gemma":[0.8984663,0.007019218,0.09385792,0.0004162962,0.0001901076,0.000004910236,0.000006395717,0.00002913994,0.000009744597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8000258,"threshold_uncertainty_score":0.972222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09756434760880943,"score_gpt":0.2931808092767436,"score_spread":0.1956164616679341,"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."}}