{"id":"W2949081601","doi":"10.48550/arxiv.1207.3362","title":"Moment-generating function method used to accurately evaluate the impact of the linearized optical noise amplified by EDFAs","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Noise (video); Moment (physics); SIGNAL (programming language); Physics; Function (biology); Power (physics); Noise power; Nonlinear system; Optics; Computer science; Acoustics; Quantum mechanics","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":[0.0006644381,0.0007419442,0.0005136511,0.0007951657,0.0003573906,0.0004389035,0.0007250928,0.0008901682,0.001020464],"category_scores_gemma":[0.002360622,0.0002385874,0.0004009613,0.0005892158,0.0004971552,0.0008023821,0.0004393499,0.0005878023,0.0003409037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006492182,"about_ca_system_score_gemma":0.0004358655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001624137,"about_ca_topic_score_gemma":0.00100083,"domain_scores_codex":[0.9996843,0.0001303384,0.00001220214,0.00002328785,0.0001215103,0.00002832665],"domain_scores_gemma":[0.9991922,0.0004901402,0.00009142836,0.00009492097,0.0001074074,0.00002381158],"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":[0.0001641769,0.00005439053,0.001272704,0.0002226618,0.00006982151,0.0006714285,0.0001649748,0.7900626,0.06243615,0.1158096,0.001364725,0.02770682],"study_design_scores_gemma":[0.000002537014,0.000006810959,0.0001417246,0.000003563489,0.000003458028,0.00002594206,0.000002639653,0.9934876,0.003384769,0.002667976,0.0002651246,0.000007844519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06940213,0.0006054313,0.9244488,0.0001590283,0.00005780507,0.0000533881,0.0001403328,0.0005574225,0.004575644],"genre_scores_gemma":[0.8660954,0.0006713911,0.1287441,0.00009025483,0.0000698423,0.0001681346,0.0001696344,0.0002898253,0.003701371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001624137,"threshold_uncertainty_score":0.004710376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1316595102232692,"score_gpt":0.2598884880637665,"score_spread":0.1282289778404973,"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."}}