{"id":"W4391086841","doi":"10.23919/ofc49934.2023.10116932","title":"Adaptive Log-Likelihood-Ratio for Optical Channels with Non-Additive-White-Gaussian-Noise","year":2023,"lang":"en","type":"article","venue":"","topic":"Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Additive white Gaussian noise; White noise; Gaussian noise; Phase-shift keying; Quadrature amplitude modulation; Computer science; Noise (video); Signal-to-noise ratio (imaging); Gaussian; Algorithm; Mathematics; Statistics; Telecommunications; Physics; Bit error rate; Artificial intelligence; Decoding methods","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000112116,0.0002693463,0.0002889582,0.0001568425,0.00007522781,0.00005887243,0.0002326751,0.0002159343,0.00007707327],"category_scores_gemma":[0.00006354052,0.0002111164,0.00007049544,0.0006313866,0.0001368279,0.0001755413,0.00008197755,0.0002395934,0.0004313517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006086717,"about_ca_system_score_gemma":0.00002097317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001675032,"about_ca_topic_score_gemma":0.00001684859,"domain_scores_codex":[0.9985723,0.000004945111,0.0002115357,0.0003202056,0.0001812511,0.0007097519],"domain_scores_gemma":[0.9992514,0.0002346989,0.00001789194,0.0003003932,0.00007036327,0.0001253129],"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":[0.0003826889,0.0002147873,0.0006331257,0.0003119048,0.000857953,0.0002415292,0.0007430238,0.378799,0.003012142,0.3054266,0.2163742,0.09300304],"study_design_scores_gemma":[0.0008917129,0.0007044293,0.001410536,0.00007715242,0.00004775506,0.000007658184,0.001173804,0.9794925,0.009590057,0.003718235,0.002260588,0.000625541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1009146,0.00007196741,0.8025639,0.002442409,0.0009333029,0.002178003,0.0001607694,0.01214654,0.0785885],"genre_scores_gemma":[0.9371808,0.00003768094,0.06082284,0.00006006485,0.0002297207,0.0004460777,0.00004725751,0.00009064136,0.001084936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8362662,"threshold_uncertainty_score":0.8609076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01340737658268635,"score_gpt":0.2205583456117834,"score_spread":0.207150969029097,"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."}}