{"id":"W2006167292","doi":"10.1109/lcomm.2012.042312.120382","title":"Low-Complexity Channel-Likelihood Estimation for Non-Binary Codes and QAM","year":2012,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Additive white Gaussian noise; Quadrature amplitude modulation; QAM; Binary number; Algorithm; Channel (broadcasting); Low-density parity-check code; Decoding methods; Computer science; Mathematics; Bit error rate; Telecommunications; Arithmetic","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":[],"consensus_categories":[],"category_scores_codex":[0.0006434673,0.000153204,0.0001599627,0.0001482648,0.0004732865,0.0001042002,0.001643943,0.00005810897,7.899042e-7],"category_scores_gemma":[0.00007418802,0.0001667056,0.0000564165,0.0002493329,0.0001906276,0.0008164691,0.0004599471,0.0001925433,0.00001334702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007718087,"about_ca_system_score_gemma":0.00002235881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006246442,"about_ca_topic_score_gemma":0.00002821925,"domain_scores_codex":[0.9989485,0.0001202664,0.0002392022,0.0002182417,0.0001261177,0.0003477146],"domain_scores_gemma":[0.9972265,0.0004501236,0.0001437678,0.002000873,0.00008003526,0.000098724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007342087,0.003136576,0.009177773,0.0008202284,0.0002602291,0.00000395588,0.04530179,0.001008915,0.4642161,0.08639958,0.1388047,0.2507966],"study_design_scores_gemma":[0.0007051141,0.0001651889,0.01544287,0.0002443153,0.00005289968,0.00005590865,0.0001448029,0.931176,0.0380506,0.01035884,0.002678307,0.0009251339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1031189,0.000146243,0.8801417,0.01504743,0.0003394638,0.0004927115,0.000007857378,0.0005144828,0.0001912085],"genre_scores_gemma":[0.6510106,0.00002441792,0.347521,0.001192225,0.0000381231,0.0001814645,0.00001487537,0.0000117439,0.000005595553],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9301671,"threshold_uncertainty_score":0.6798058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05337486829965723,"score_gpt":0.315533758078992,"score_spread":0.2621588897793348,"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."}}