{"id":"W2120460089","doi":"10.1109/cjece.2004.1425794","title":"Multilevel code design for multistage and parallel decoding schemes for rayleigh fading channels","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Decoding methods; Rayleigh fading; Additive white Gaussian noise; Fading; Algorithm; Computer science; Channel (broadcasting); Channel state information; Fading distribution; Channel capacity; Gaussian; Electronic engineering; Theoretical computer science; Telecommunications; Mathematics; Engineering; Wireless; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001216119,0.0001432027,0.0002241769,0.0002422619,0.00009324896,0.00006119056,0.0001450057,0.00006796158,4.56319e-7],"category_scores_gemma":[0.00006340211,0.0001501612,0.00004891663,0.00008384269,0.00001646845,0.0001521769,0.00000938276,0.0001688949,6.577591e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001282124,"about_ca_system_score_gemma":0.00005242755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001413523,"about_ca_topic_score_gemma":0.00002356132,"domain_scores_codex":[0.9993005,0.000005198055,0.0002548846,0.00009626436,0.00004200183,0.0003011979],"domain_scores_gemma":[0.9992467,0.0002679802,0.00004483854,0.00007529535,0.00007217252,0.0002930012],"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":[0.000008586427,0.00000424766,0.00002259185,0.00008630044,0.00004709947,0.000007402514,0.0001858135,0.9521823,0.001847784,0.004129927,0.00009321719,0.04138474],"study_design_scores_gemma":[0.000771304,0.0001246246,0.00006403736,0.000128488,0.00001132284,0.00007038754,0.000003103183,0.9875706,0.005898762,0.001018267,0.004144951,0.0001941258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01699928,0.002373938,0.9801677,0.00004745028,0.0001099396,0.000231999,0.000007230968,0.00006083422,0.000001597199],"genre_scores_gemma":[0.5744116,0.0001388849,0.4253037,0.00001845187,0.00008291165,0.00001866799,0.000001125433,0.00002254174,0.000002138188],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5574123,"threshold_uncertainty_score":0.6123397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02425752823803079,"score_gpt":0.2304876132129677,"score_spread":0.2062300849749369,"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."}}