{"id":"W4414153861","doi":"10.1109/jetcas.2025.3608825","title":"Research on QC-LDPC Decoding Method With Low Quantization Word Length Based on Adaptive Information Mapping in Passive Optical Network","year":2025,"lang":"en","type":"article","venue":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","topic":"Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Decoding methods; Coding gain; List decoding; Quantization (signal processing); Adaptive coding; Sequential decoding; Linear network coding; Coding (social sciences)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000310734,0.0003619505,0.0003005711,0.0003929047,0.0004484312,0.0004766471,0.0006398262,0.0004657912,0.000992779],"category_scores_gemma":[0.001121014,0.0001949973,0.0002542526,0.0005821513,0.0005510582,0.001302835,0.0003270697,0.0005423101,0.0002370139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008386804,"about_ca_system_score_gemma":0.001000522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004183567,"about_ca_topic_score_gemma":0.002763339,"domain_scores_codex":[0.9995628,0.00005736251,0.00001900555,0.00008556205,0.0002473948,0.00002789702],"domain_scores_gemma":[0.9995117,0.0001614416,0.00005378494,0.0000466978,0.0002097404,0.00001671521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002864274,0.0002334538,0.003302902,0.0008547534,0.00008238357,0.0005017113,0.0006269795,0.1899276,0.3428283,0.09801819,0.00343821,0.359899],"study_design_scores_gemma":[0.00002621001,0.0001472747,0.0004747845,0.00002505056,0.00002102764,0.0002815896,0.00003211244,0.9027354,0.08826797,0.003298195,0.004645589,0.0000448848],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09076548,0.003493957,0.8902534,0.0006249576,0.0002424073,0.0001238118,0.00006172628,0.0005703233,0.01386391],"genre_scores_gemma":[0.7435085,0.002902563,0.2448699,0.000173584,0.00008996434,0.00009372492,0.00008789129,0.00006415998,0.008209842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004183567,"threshold_uncertainty_score":0.008318424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0304985981127602,"score_gpt":0.2979077763353293,"score_spread":0.2674091782225691,"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."}}