{"id":"W2121369649","doi":"10.1186/1687-1499-2011-175","title":"Uncertainty area-based interference mitigation for cognitive radio","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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Prompt (Canada); Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Computer science; Cognitive radio; Mobile ad hoc network; Underlay; Interference (communication); Computer network; Wireless network; Cognitive network; Wireless; Wireless ad hoc network; Telecommunications; Distributed computing; Signal-to-noise ratio (imaging); Channel (broadcasting)","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":[0.0006307496,0.0001907975,0.0002297204,0.0001545819,0.001004499,0.0003234244,0.0007681845,0.00006321423,0.000004964982],"category_scores_gemma":[0.00003334258,0.000173728,0.0001083598,0.0003096075,0.0001608839,0.0003076294,0.0001527257,0.000466897,0.000001680303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000655162,"about_ca_system_score_gemma":0.00007267345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007817,"about_ca_topic_score_gemma":0.00004073668,"domain_scores_codex":[0.9986281,0.0002716778,0.0003644289,0.0002670938,0.0001432883,0.0003253904],"domain_scores_gemma":[0.9975565,0.001149107,0.0003137849,0.0005249671,0.0003033131,0.0001523144],"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":[0.0001260084,0.000161285,0.00211195,0.000008419775,0.00007895133,0.00001340103,0.00169821,0.0001770151,0.00009731983,0.02091876,0.0001170591,0.9744916],"study_design_scores_gemma":[0.001080206,0.0004922095,0.002598316,0.001059751,0.00004041607,0.00013544,0.0001776102,0.9856439,0.0002762226,0.00602442,0.002125181,0.0003463322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05411279,0.001697561,0.9400513,0.0009531688,0.0004121742,0.0002861197,0.00000385241,0.00008056546,0.002402431],"genre_scores_gemma":[0.9820664,0.001521362,0.01561164,0.000542253,0.000201318,0.00001838855,0.00001020569,0.00001666469,0.00001175727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9854669,"threshold_uncertainty_score":0.7725899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06940082687591168,"score_gpt":0.2805006980094982,"score_spread":0.2110998711335865,"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."}}