{"id":"W2102268535","doi":"10.1109/iccw.2008.95","title":"The Impact of Inaccurate Sensing Information in Cognitive Wireless Personal Area Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Federation for the Humanities and Social Sciences","keywords":"Cognitive radio; Computer science; Channel (broadcasting); Set (abstract data type); Probabilistic logic; Computer network; Wireless; Process (computing); Cognition; Personal area network; Wireless network; Telecommunications; Real-time computing; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002575284,0.000122564,0.0001606078,0.00009355908,0.0002367229,0.00009665297,0.0001468452,0.00004691472,0.000004109097],"category_scores_gemma":[0.00004598629,0.0000799761,0.00009562707,0.0005579304,0.0001114856,0.0008099547,0.00007968938,0.0001858053,0.000002705711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007831489,"about_ca_system_score_gemma":0.0001088061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003942012,"about_ca_topic_score_gemma":0.000123525,"domain_scores_codex":[0.9990381,0.00007231992,0.0002810908,0.0001283173,0.0001756612,0.0003045055],"domain_scores_gemma":[0.9990361,0.0004656718,0.0001386602,0.0001176972,0.0001915485,0.00005032199],"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":[0.0001967164,0.00007049892,0.01640051,0.00000559301,0.0001173523,0.0001178896,0.0108628,0.02308644,0.00009748833,0.002847895,0.0009135851,0.9452832],"study_design_scores_gemma":[0.0003503634,0.00006381344,0.04646688,0.00005052579,0.000001856852,0.0001339129,0.000271601,0.9523196,0.00009361462,0.0001343886,0.00000692849,0.0001065304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6096081,0.00005062037,0.3873884,0.00006866628,0.0000595856,0.00009777351,7.446858e-7,0.00002592232,0.00270026],"genre_scores_gemma":[0.9993904,0.0001390082,0.0003354729,0.00007201598,0.00003911268,6.583533e-7,0.000003559233,0.00000344914,0.00001629336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9451767,"threshold_uncertainty_score":0.3261331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02115774845786861,"score_gpt":0.254595372643137,"score_spread":0.2334376241852684,"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."}}