{"id":"W4299048940","doi":"10.48550/arxiv.1603.03725","title":"A Multi-Channel Spectrum Sensing Fusion Mechanism for Cognitive Radio\\n Networks: Design and Application to IEEE 802.22 WRANs","year":2016,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Cognitive radio; Computer science; False alarm; IEEE 802.11; Computer network; Wireless; Bandwidth (computing); Radio spectrum; Interference (communication); Channel (broadcasting); Transmission (telecommunications); Wireless network; Telecommunications; Artificial intelligence","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":[0.0008541356,0.0009324173,0.0008595801,0.0005276994,0.001175408,0.0004324395,0.0009351984,0.0007987264,0.00001418932],"category_scores_gemma":[0.00007452293,0.001049248,0.0004086106,0.001277997,0.0002328429,0.0005199929,0.0009199989,0.0006577526,0.00007366162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00048938,"about_ca_system_score_gemma":0.0001810945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001183573,"about_ca_topic_score_gemma":0.00005750496,"domain_scores_codex":[0.9943024,0.0004668545,0.0006018733,0.003209398,0.0002049488,0.001214537],"domain_scores_gemma":[0.9957473,0.0008048398,0.0006790413,0.001086633,0.0008441722,0.000838026],"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":[0.0009494125,0.0002247345,0.000004151517,0.00007533628,0.0003115777,0.0001452803,0.0004842793,0.9641177,0.0006831058,0.01255674,0.000289598,0.02015807],"study_design_scores_gemma":[0.002941153,0.0004909978,0.00004265929,0.0005188754,0.0003081901,0.00004998966,0.0002420343,0.9615327,0.001696674,0.03080819,0.0001432793,0.001225294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003747091,0.00006480233,0.9888427,0.0002741603,0.002384939,0.004137795,0.0001451114,0.0003394808,0.00006394394],"genre_scores_gemma":[0.9723973,0.00079161,0.02483236,0.0003037527,0.000647275,0.00001445629,0.00003506374,0.00008248294,0.0008956775],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9686502,"threshold_uncertainty_score":0.9991958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04926562853330424,"score_gpt":0.1987064151881878,"score_spread":0.1494407866548836,"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."}}