{"id":"W1998502708","doi":"10.1364/ao.39.001761","title":"Bandwidth optimization of optical data links by use of error-control codes","year":2000,"lang":"en","type":"article","venue":"Applied Optics","topic":"Semiconductor Lasers and Optical Devices","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Error detection and correction; Bandwidth (computing); Bit error rate; Wavelength-division multiplexing; Electronic engineering; Forward error correction; Multiplexing; Algorithm; Optics; Wavelength; Telecommunications; Decoding methods; 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":[0.000414536,0.0004774527,0.0002317982,0.0006107812,0.0003361437,0.0005787883,0.0007041087,0.0004266135,0.0006471355],"category_scores_gemma":[0.001427361,0.0001810303,0.0001406757,0.0004788743,0.0004050016,0.0007168958,0.0003617102,0.0004340817,0.0002204096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004538385,"about_ca_system_score_gemma":0.0003525619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003751882,"about_ca_topic_score_gemma":0.0005983622,"domain_scores_codex":[0.9996521,0.00008734458,0.00001835399,0.00003785417,0.0001566596,0.00004757028],"domain_scores_gemma":[0.9992685,0.000262804,0.0001306869,0.00006955974,0.0002469479,0.00002159017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000293866,0.0001812389,0.001450261,0.0003439917,0.00006269246,0.0002208021,0.0001833989,0.3253026,0.2872066,0.1331987,0.001433973,0.2501218],"study_design_scores_gemma":[0.00004905183,0.0002753137,0.0003521521,0.00003756858,0.00003752837,0.0003385125,0.00003123546,0.82201,0.1501806,0.01854858,0.008095251,0.0000442786],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09936997,0.001169169,0.8920031,0.0002313935,0.00005492778,0.00006606046,0.00004304367,0.0004676031,0.006594691],"genre_scores_gemma":[0.771054,0.0007084876,0.2252393,0.00006493823,0.00004334801,0.0001461622,0.00005513596,0.00006035546,0.002628239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007041087,"threshold_uncertainty_score":0.003292918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02775584644564624,"score_gpt":0.2334202652097076,"score_spread":0.2056644187640614,"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."}}