{"id":"W2123380295","doi":"10.1109/tbc.2007.912849","title":"Combine LDPC Codes Over GF(q) With q-ary Modulations for Bandwidth Efficient Transmission","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Institut National de la Recherche Scientifique","funders":"","keywords":"Low-density parity-check code; Computer science; Additive white Gaussian noise; Turbo code; Forward error correction; Transmission (telecommunications); Error detection and correction; Digital Video Broadcasting; Bandwidth (computing); Bit error rate; Concatenated error correction code; Algorithm; Electronic engineering; Decoding methods; Channel (broadcasting); Block code; Telecommunications; 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":[0.0002438442,0.0002807897,0.0002666873,0.0002978863,0.001043955,0.00006674165,0.0004369838,0.0001010875,0.00002180992],"category_scores_gemma":[0.00001577334,0.0002513447,0.0001518287,0.0006731307,0.00008766365,0.0003042042,0.000003428243,0.0003279134,0.000006504275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001067928,"about_ca_system_score_gemma":0.0001298666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006382959,"about_ca_topic_score_gemma":0.0000179862,"domain_scores_codex":[0.9981649,0.0000655106,0.0003543903,0.0005888483,0.0004020959,0.0004242412],"domain_scores_gemma":[0.9985324,0.000473218,0.000125042,0.0005265997,0.0001670139,0.0001757327],"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":[0.0002565876,0.00119281,0.000185187,0.0001099574,0.00009373183,0.00004667877,0.004229061,0.7706528,0.02667223,0.0005342807,0.0005310935,0.1954956],"study_design_scores_gemma":[0.001082423,0.0005679656,0.0005096289,0.0002412599,0.00003224909,0.000201311,0.00002582684,0.9200283,0.07633178,0.0001512693,0.0004127978,0.0004151286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09230734,0.00003175026,0.9050639,0.0002253755,0.000312817,0.0005573143,0.00001210929,0.001083409,0.0004060295],"genre_scores_gemma":[0.8097668,0.000009540116,0.1896831,0.000115181,0.00002980276,0.0001095473,0.000002381401,0.00003500475,0.0002486582],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7174594,"threshold_uncertainty_score":0.9999939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02760566001082492,"score_gpt":0.2557139146250871,"score_spread":0.2281082546142622,"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."}}