{"id":"W2117070041","doi":"10.1109/lpt.2011.2162231","title":"Low-Density Parity-Check Coding in Ultra-Wideband-Over-Fiber Systems","year":2011,"lang":"en","type":"article","venue":"IEEE Photonics Technology Letters","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Low-density parity-check code; Coding gain; Coding (social sciences); Ultra-wideband; Bit error rate; Computer science; Word error rate; Code rate; Algorithm; Parity bit; Parity (physics); Decoding methods; Forward error correction; Electronic engineering; Theoretical computer science; Physics; Telecommunications; Mathematics; Speech recognition; Statistics; Engineering; Atomic physics","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":[0.0007071078,0.0002291739,0.0002921389,0.0002043921,0.0003058241,0.0003735329,0.0003089976,0.0004610263,0.0005822176],"category_scores_gemma":[0.001719611,0.0001407286,0.0001301138,0.0002355905,0.0004286534,0.00055676,0.0002694036,0.0002458317,0.0001421026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003963879,"about_ca_system_score_gemma":0.0003100939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00122487,"about_ca_topic_score_gemma":0.001341072,"domain_scores_codex":[0.9995042,0.0002273909,0.00001334999,0.00002521773,0.0001766375,0.00005319663],"domain_scores_gemma":[0.9987067,0.0008463755,0.0001401194,0.00009251539,0.0001873345,0.00002685479],"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":[0.001068795,0.000166122,0.006142894,0.0003281343,0.0001183229,0.001161612,0.0004229963,0.6556412,0.2498963,0.04761741,0.0007344253,0.03670173],"study_design_scores_gemma":[0.00005103408,0.0002202156,0.0009748974,0.00002066038,0.0000310375,0.0002555754,0.000030534,0.9265647,0.06602176,0.004885214,0.0009169399,0.00002742322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8559251,0.0004755876,0.1386469,0.0002850603,0.0000226833,0.00002778784,0.00005218391,0.0003306502,0.004234024],"genre_scores_gemma":[0.993152,0.00007403339,0.006495957,0.00001562169,0.000003718433,0.000006191956,0.0000102543,0.000005078135,0.0002372446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00122487,"threshold_uncertainty_score":0.003739595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0135229059130428,"score_gpt":0.2066954946930834,"score_spread":0.1931725887800406,"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."}}