{"id":"W276985235","doi":"10.1109/twc.2008.060642","title":"Robust switching blind equalizer for wireless cognitive receivers","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Communications Research Centre Canada","funders":"","keywords":"Computer science; Equalizer; Wireless; Cognitive radio; Adaptive equalizer; Computer network; Electronic engineering; Telecommunications; Engineering; 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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004482698,0.0002623349,0.0002886749,0.0003926211,0.001562014,0.000121986,0.002023417,0.0001786845,0.00001502909],"category_scores_gemma":[0.00001637368,0.000290216,0.0002228219,0.000859705,0.0002256388,0.0008926594,0.00002159757,0.0006025325,0.00005376598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001123097,"about_ca_system_score_gemma":0.0002217564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007789169,"about_ca_topic_score_gemma":0.0002131273,"domain_scores_codex":[0.998044,0.0003502423,0.0004765854,0.0004722724,0.0003193935,0.0003374984],"domain_scores_gemma":[0.9959711,0.00131151,0.0002070467,0.001941192,0.000425349,0.0001438417],"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":[0.0005801182,0.006167705,0.00007538079,0.0001129607,0.0008542554,0.0000167419,0.05832054,0.02252475,0.01357182,0.2313201,0.002869594,0.6635861],"study_design_scores_gemma":[0.006822033,0.0008170576,0.0003262604,0.0005145838,0.0001926648,0.0001642469,0.001922204,0.8336358,0.1422593,0.004468605,0.006717314,0.002159899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01773936,0.00006269322,0.9763047,0.002459596,0.0001965053,0.0008660695,0.00005125314,0.0008210466,0.001498767],"genre_scores_gemma":[0.9103299,0.0008270866,0.08691706,0.0007083438,0.00001709845,0.000681238,0.00001954475,0.00003756489,0.0004621096],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8925906,"threshold_uncertainty_score":0.999955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1121562108123677,"score_gpt":0.3210912249680954,"score_spread":0.2089350141557277,"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."}}