{"id":"W2162534832","doi":"10.1109/glocom.2009.5425995","title":"Adaptive Lp-Norm Metric for Secondary BICM-OFDM Systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Algorithm; Orthogonal frequency-division multiplexing; Decoding methods; Computer science; Norm (philosophy); Bit error rate; Robustness (evolution); Mathematics; Channel (broadcasting); Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001833133,0.001269386,0.001106396,0.0008036409,0.0004446071,0.001335619,0.001274855,0.001119726,0.001375805],"category_scores_gemma":[0.01021085,0.0003197323,0.0003611305,0.0006671762,0.000983633,0.00126378,0.001639079,0.001412214,0.0007946055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001086153,"about_ca_system_score_gemma":0.001473471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001242668,"about_ca_topic_score_gemma":0.0009987325,"domain_scores_codex":[0.9981868,0.0007488113,0.00009542082,0.0002341917,0.0006219556,0.0001128626],"domain_scores_gemma":[0.997541,0.001222226,0.0002566652,0.0001997592,0.0006728467,0.0001076106],"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":[0.0003143046,0.0001167357,0.0008264531,0.0002209099,0.00004291225,0.0001494162,0.0001965756,0.6544068,0.02112026,0.08976945,0.00443185,0.2284043],"study_design_scores_gemma":[0.000004413044,0.000043785,0.0001150708,0.000008583856,0.000002747396,0.00005055021,0.000007768058,0.9888676,0.003449217,0.006585192,0.0008505763,0.00001457042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006203729,0.0001612128,0.9921354,0.0001045504,0.00002255156,0.00002301945,0.00003380663,0.0001598915,0.00115579],"genre_scores_gemma":[0.306704,0.0004283254,0.6883305,0.0001505073,0.00009701923,0.0002280865,0.0003684678,0.000139201,0.003553939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001833133,"threshold_uncertainty_score":0.009694636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01664263319947275,"score_gpt":0.2502588431528856,"score_spread":0.2336162099534128,"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."}}