{"id":"W2171915411","doi":"10.1109/tcomm.2010.101210.090367","title":"Robust L_p-Norm Decoding for BICM-Based Secondary User Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Weibull fading; Decoding methods; Fading; Algorithm; Computer science; Nakagami distribution; Gaussian noise; Rayleigh fading; Bit error rate; Mathematics; Electronic engineering; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.0002446788,0.0002450159,0.0002467766,0.000432891,0.0008104211,0.00009387772,0.001793984,0.0003012507,0.00008174399],"category_scores_gemma":[0.0000195118,0.0002796635,0.0001689846,0.000494886,0.0002214025,0.0002152756,0.000006638236,0.00128098,0.00008567866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001076484,"about_ca_system_score_gemma":0.00009197884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006222826,"about_ca_topic_score_gemma":0.001653511,"domain_scores_codex":[0.9988239,0.00005980586,0.0004585098,0.0002129284,0.0001145747,0.0003302246],"domain_scores_gemma":[0.9946739,0.0009430135,0.00006723491,0.004057244,0.0001574343,0.0001011772],"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":[0.00005162579,0.001423793,0.00009363813,0.0003094447,0.000597685,0.000001145263,0.0005858338,0.7348461,0.1224043,0.02416055,0.007490016,0.1080358],"study_design_scores_gemma":[0.001268912,0.00009255032,0.00007527611,0.00008745183,0.0001574823,0.00002959701,0.0003199288,0.7802073,0.03983461,0.0004060573,0.1768213,0.0006995265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009325087,0.0003914963,0.9789495,0.001589568,0.001135639,0.0008683878,0.0002596694,0.001786142,0.005694469],"genre_scores_gemma":[0.9371021,0.0002738915,0.06045705,0.00008509804,0.00002500158,0.001605991,0.00005957057,0.00008768372,0.0003035591],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9277771,"threshold_uncertainty_score":0.9999655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03296156520620366,"score_gpt":0.2444072024845561,"score_spread":0.2114456372783524,"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."}}