{"id":"W4390485514","doi":"10.1109/acp/poem59049.2023.10369637","title":"Performance Improvement by Channel gOSNR Waterfilling","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Photonic Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Channel (broadcasting); Computer network","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":[0.0005378061,0.0007027013,0.0003076961,0.0002600753,0.0003284208,0.0007478779,0.0003611289,0.0004656549,0.001662672],"category_scores_gemma":[0.001112671,0.0001033462,0.0001765561,0.0003807487,0.0008417211,0.0007041838,0.0004673247,0.0003613982,0.000428185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005968206,"about_ca_system_score_gemma":0.0003974553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004754807,"about_ca_topic_score_gemma":0.0007675089,"domain_scores_codex":[0.9996356,0.00007167588,0.0000114365,0.00006095544,0.0001110487,0.0001092665],"domain_scores_gemma":[0.9994098,0.00030318,0.0001002634,0.000070217,0.00008801382,0.00002841242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004037769,0.0000943129,0.002073251,0.0002682022,0.0000356331,0.0003958531,0.0001902437,0.0797698,0.8283611,0.03603486,0.001573183,0.05079978],"study_design_scores_gemma":[0.00002688138,0.0003711578,0.001663684,0.00004892244,0.00004533822,0.0002314364,0.00007682171,0.3386757,0.6382334,0.01444077,0.006116108,0.00006986936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7601356,0.001515085,0.1947336,0.0007961005,0.0001886896,0.00005365754,0.0002778869,0.00340581,0.03889355],"genre_scores_gemma":[0.9876269,0.0004385742,0.01049591,0.00006440168,0.00001714986,0.000009774352,0.0000443723,0.00008396992,0.001219008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001662672,"threshold_uncertainty_score":0.005562186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293963300202556,"score_gpt":0.2141678939714362,"score_spread":0.2012282609694107,"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."}}