{"id":"W2916730143","doi":"10.1109/glocom.2018.8648118","title":"Distributed Learning-Based Multi-Band Multi-User Cooperative Sensing in Cognitive Radio Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Cognitive radio; Computer science; Channel (broadcasting); Selection (genetic algorithm); Scheme (mathematics); Constraint (computer-aided design); Scheduling (production processes); Computer network; Distributed computing; Optimization problem; Control channel; Wireless; Mathematical optimization; Telecommunications; Artificial intelligence; Algorithm; Engineering; Base station","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004837604,0.0003632035,0.0004173709,0.0001955769,0.0004493909,0.0003663651,0.0002785798,0.0001650199,0.00005054782],"category_scores_gemma":[0.0002831539,0.0003335631,0.0001006686,0.001232096,0.0003118392,0.0003969617,0.000119445,0.0005946438,0.00003897184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001476524,"about_ca_system_score_gemma":0.0001258297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000182036,"about_ca_topic_score_gemma":0.002079693,"domain_scores_codex":[0.9973134,0.0004165938,0.0004066909,0.0008399824,0.0002356973,0.0007875846],"domain_scores_gemma":[0.9982547,0.0006108333,0.0001337586,0.0002826838,0.0005249938,0.0001930425],"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":[0.001470941,0.003503813,0.1360442,0.0000636655,0.0008957465,0.00393421,0.01401208,0.4674217,0.005894756,0.006891315,0.005495029,0.3543726],"study_design_scores_gemma":[0.003232396,0.0002303791,0.01474712,0.0001777988,0.00001333234,0.00002766033,0.000185357,0.9756447,0.00475243,0.000009841183,0.0004992862,0.0004796831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04024798,0.0001250878,0.9577214,0.0002719164,0.0003436783,0.0003952883,0.000004713665,0.0002993273,0.0005906752],"genre_scores_gemma":[0.9764667,0.00001352292,0.02201775,0.0006685855,0.0002528107,0.000003676013,0.00003919457,0.00002850665,0.0005092554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9362187,"threshold_uncertainty_score":0.9999117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02237431368242265,"score_gpt":0.2692589323003811,"score_spread":0.2468846186179584,"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."}}