{"id":"W4396680728","doi":"10.1109/access.2024.3390209","title":"Performance Evaluation of Optical Transmission Based on Link Estimation by Using Deep Learning Techniques","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Optical Network Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optiwave Systems (Canada)","funders":"","keywords":"Computer science; Transmission (telecommunications); Metric (unit); Mean squared error; Keying; Modulation (music); Bit error rate; Artificial intelligence; Algorithm; Channel (broadcasting); Telecommunications; Mathematics; Statistics","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":[0.001050131,0.0008436205,0.0003585001,0.0008658622,0.000243607,0.0005399365,0.0005942474,0.0005349266,0.00111598],"category_scores_gemma":[0.003749734,0.0001469526,0.0002740562,0.0004520792,0.0002855724,0.001026827,0.0006290865,0.0005495813,0.0002741118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006212075,"about_ca_system_score_gemma":0.0004829045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004511407,"about_ca_topic_score_gemma":0.004276322,"domain_scores_codex":[0.9995159,0.0001062701,0.0000328965,0.00007801329,0.0001935113,0.000073491],"domain_scores_gemma":[0.9985538,0.0005728854,0.000245219,0.0001132367,0.0004662154,0.00004854074],"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":[0.001047908,0.0003556294,0.01208007,0.0002246228,0.0002036637,0.0002175941,0.000103844,0.5977013,0.04362157,0.00190665,0.001656134,0.340881],"study_design_scores_gemma":[0.00000467675,0.00009709049,0.0007943782,0.000006621378,0.00001231735,0.00003606838,0.000008418936,0.9854989,0.01314844,0.000242888,0.0001425108,0.000007768709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6394404,0.001270822,0.3494283,0.0002852458,0.0001163822,0.00005130614,0.0002174047,0.004152184,0.005037947],"genre_scores_gemma":[0.9728151,0.0001659349,0.02559371,0.00005095666,0.00001079478,0.00001355119,0.0001861732,0.00005112441,0.001112655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004511407,"threshold_uncertainty_score":0.00897032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03091443367188079,"score_gpt":0.3200724850886353,"score_spread":0.2891580514167545,"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."}}