{"id":"W2188775013","doi":"","title":"Forensic Outlier Detection and Penalty Analysis To Regulate Cognitive Radio Network","year":2014,"lang":"en","type":"article","venue":"QSpace (Queen's University Library)","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Computer science; Outlier; Cognition; Anomaly detection; Artificial intelligence; Computer security; Psychology; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002814621,0.000849246,0.00128569,0.002133769,0.0008389577,0.00135332,0.001470678,0.0009896264,0.001484623],"category_scores_gemma":[0.01596858,0.0003160148,0.0005979275,0.001804209,0.0009519174,0.001610324,0.001988878,0.001746085,0.0004996607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007363619,"about_ca_system_score_gemma":0.001619956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002206838,"about_ca_topic_score_gemma":0.002191281,"domain_scores_codex":[0.9983159,0.0004691239,0.00009756092,0.0003089138,0.0006039718,0.0002045451],"domain_scores_gemma":[0.9943072,0.002444552,0.0007095184,0.0007995713,0.001393964,0.0003452794],"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":[0.0009579094,0.0004189127,0.01076912,0.0001412396,0.000156475,0.0004691474,0.0002226556,0.5278687,0.01436118,0.04327811,0.005755984,0.3956006],"study_design_scores_gemma":[0.000006441617,0.00002957001,0.0004604991,0.000005166658,0.000004980796,0.00005174822,0.00002142282,0.9916033,0.001181648,0.006255239,0.0003719719,0.000007976151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02628715,0.0001619657,0.9716589,0.0002104744,0.00007983413,0.00005391132,0.00007550568,0.0005574205,0.0009149062],"genre_scores_gemma":[0.7723775,0.0002592704,0.2233252,0.0001189683,0.0002146817,0.0001197425,0.0004798247,0.0001635594,0.002941271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002814621,"threshold_uncertainty_score":0.01488531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004853916115113916,"score_gpt":0.1734900168697009,"score_spread":0.168636100754587,"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."}}