{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001283271,0.0002373645,0.0003644397,0.0004634081,0.0004447096,0.0002555729,0.0003147752,0.0001017001,0.00002563014],"category_scores_gemma":[0.00002749012,0.0002611908,0.0001695585,0.002409784,0.00009164584,0.001299432,0.000438628,0.0002129126,0.00001974411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005749543,"about_ca_system_score_gemma":0.00003203608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005660121,"about_ca_topic_score_gemma":0.0002738444,"domain_scores_codex":[0.9982061,0.0002657733,0.0001296132,0.000697749,0.000218345,0.0004824335],"domain_scores_gemma":[0.9988486,0.0002663907,0.0001112952,0.0003684493,0.00006650866,0.0003387842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00151125,0.0003016828,0.1360073,0.00008730306,0.005518798,0.0009971705,0.006762129,0.02857562,0.00008931372,0.09166381,0.0811978,0.6472878],"study_design_scores_gemma":[0.002763273,0.001086972,0.7349736,0.0002521249,0.001771599,0.00001704053,0.0007238211,0.1733226,0.003317691,0.007039697,0.07232044,0.002411181],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2268073,0.00001772836,0.7540776,0.01456706,0.0001753338,0.0002444239,0.000004223529,0.0003437612,0.003762545],"genre_scores_gemma":[0.9811431,0.00003957653,0.01229959,0.0005404548,0.0001994498,3.643982e-7,0.000008219153,0.00001766585,0.005751572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7543358,"threshold_uncertainty_score":0.999984,"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."}}