{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005906164,0.0003373208,0.0003957968,0.0003667233,0.0002594473,0.0006941289,0.0004997336,0.0005468701,0.001480916],"category_scores_gemma":[0.00311366,0.0002059516,0.0003606652,0.0004750639,0.0004071427,0.0006513135,0.0005515025,0.0005596766,0.0004123728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006713392,"about_ca_system_score_gemma":0.0008110297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000955563,"about_ca_topic_score_gemma":0.002034729,"domain_scores_codex":[0.999343,0.0001765321,0.0000457752,0.00006735901,0.000311744,0.00005563761],"domain_scores_gemma":[0.9988403,0.0004701598,0.0001792312,0.0001411,0.0003409899,0.00002830457],"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":[0.0001924695,0.00005829007,0.001292521,0.0003330105,0.00006693328,0.000179051,0.000312967,0.478072,0.06150843,0.2186213,0.002203775,0.2371591],"study_design_scores_gemma":[0.00002126953,0.00009966827,0.0002378393,0.0000359071,0.00002664313,0.0001784349,0.00002340117,0.9448353,0.02173031,0.02815531,0.00463052,0.00002539853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01764984,0.0002817348,0.9791902,0.00009628697,0.00001723356,0.00004360102,0.0000467631,0.0001173026,0.002556929],"genre_scores_gemma":[0.5119014,0.0005958567,0.4846554,0.00007794077,0.00003606033,0.0001652349,0.0001172871,0.00005961764,0.002391176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001480916,"threshold_uncertainty_score":0.0049541,"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."}}