{"id":"W2087488579","doi":"10.1117/12.2001664","title":"A monolithic optical front-end for soft-decision LDPC decoders","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Low-density parity-check code; Computer science; Front and back ends; Phase-shift keying; Forward error correction; Decoding methods; Keying; Multiplexing; Electronic engineering; Optics; Physics; Bit error rate; Algorithm; Telecommunications","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":[0.0002890716,0.0004419709,0.0003382023,0.0004046867,0.0004721946,0.0008352654,0.001393816,0.001171356,0.002892191],"category_scores_gemma":[0.0005293768,0.0004065287,0.0002541998,0.0002237425,0.0004111975,0.001045969,0.0004816853,0.0009316853,0.002027049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005836244,"about_ca_system_score_gemma":0.0003881133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003888701,"about_ca_topic_score_gemma":0.001021153,"domain_scores_codex":[0.9996075,0.00003368231,0.00001257356,0.00007977594,0.0002263849,0.00003999205],"domain_scores_gemma":[0.9994969,0.0001541151,0.00004517708,0.0000673611,0.0001994148,0.00003699196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003882593,0.0002293622,0.001329989,0.000254093,0.00005568964,0.0002765072,0.00008971919,0.007722409,0.8700712,0.01216142,0.003242095,0.1041793],"study_design_scores_gemma":[0.0000805332,0.0005178467,0.002078855,0.00003457215,0.00006845273,0.0006548802,0.00002949154,0.1491253,0.8173609,0.00244528,0.02754176,0.00006205605],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1030976,0.001331486,0.882279,0.0005460108,0.0004023891,0.0001602814,0.0002728069,0.003624202,0.008286298],"genre_scores_gemma":[0.5297896,0.0003818609,0.4510132,0.0008213249,0.0002079571,0.00009464617,0.000317142,0.0001461918,0.01722801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002892191,"threshold_uncertainty_score":0.009675384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01651852858881877,"score_gpt":0.2565284839641146,"score_spread":0.2400099553752958,"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."}}