{"id":"W1503671122","doi":"10.1364/jocn.7.000885","title":"Analysis of Low-Bit Soft-Decision Error Correction in Optical Front Ends","year":2015,"lang":"en","type":"article","venue":"Journal of Optical Communications and Networking","topic":"Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Decoding methods; Computer science; Error detection and correction; Front and back ends; Noise (video); Code (set theory); Bit error rate; Electronic engineering; Algorithm; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008418095,0.000123671,0.0004893602,0.0004747676,0.00004403684,0.00003935283,0.0004634653,0.0001642023,0.000006763547],"category_scores_gemma":[0.0002121612,0.0001086924,0.0001249438,0.0009225567,0.0001898843,0.0001617855,0.0002330201,0.0006419697,0.000001484338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001164715,"about_ca_system_score_gemma":0.00002368801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003654693,"about_ca_topic_score_gemma":0.00005797053,"domain_scores_codex":[0.9986301,0.00004783918,0.0007773878,0.0000909239,0.0002401617,0.0002135682],"domain_scores_gemma":[0.9981753,0.0008728161,0.0001341278,0.0005136376,0.0001672176,0.0001369006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001621269,0.0003264803,0.027769,0.000023174,0.0006775261,0.00001769947,0.0004428726,0.3774628,0.0002280782,0.003575914,0.0006994835,0.5886148],"study_design_scores_gemma":[0.0003681103,0.0001572922,0.008534144,0.000202542,0.0002678144,0.00002261039,0.0003314819,0.987867,0.00003342226,0.0008140991,0.001279035,0.000122452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8592036,0.01213533,0.1204614,0.000575086,0.0009160682,0.0001433963,0.000002086819,0.0001114874,0.00645152],"genre_scores_gemma":[0.9458299,0.002169622,0.0518992,0.000010747,0.00006674288,0.000002658951,0.000002486345,0.00001343142,0.000005236094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6104041,"threshold_uncertainty_score":0.4432349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04009684339550698,"score_gpt":0.2886097660504083,"score_spread":0.2485129226549013,"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."}}