{"id":"W2124047962","doi":"10.1364/oe.18.003919","title":"Data-aided adaptive weighted channel equalizer for coherent optical OFDM","year":2010,"lang":"en","type":"article","venue":"Optics Express","topic":"Optical Network Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthogonal frequency-division multiplexing; Computer science; Phase noise; Polarization mode dispersion; Adaptive equalizer; Electronic engineering; Channel (broadcasting); Equalization (audio); Transmission (telecommunications); Telecommunications; Optical fiber; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001996366,0.0002869557,0.0003424974,0.00007317012,0.00007248943,0.00008456178,0.0009763503,0.0003705436,0.00005537233],"category_scores_gemma":[0.0002033925,0.0002661586,0.00006802119,0.0001426514,0.0001844227,0.0002205675,0.000418591,0.0005524113,0.00006267051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002637773,"about_ca_system_score_gemma":0.0000150325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001798751,"about_ca_topic_score_gemma":0.00001503286,"domain_scores_codex":[0.9984182,0.00001019448,0.0003392021,0.0004098832,0.000230725,0.000591779],"domain_scores_gemma":[0.9980705,0.0003881652,0.0000382334,0.0012451,0.0001080267,0.0001499733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002017429,0.0004200503,0.00002267882,0.0002995908,0.0005960609,0.0000430869,0.0002588417,0.01327577,0.09653111,0.8046506,0.06307322,0.02062722],"study_design_scores_gemma":[0.0007276874,0.0001113764,0.00001654145,0.0000359981,0.00007465731,0.000005733082,0.0001059719,0.9363598,0.02285755,0.00835234,0.03084443,0.0005078907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2615404,0.0006045749,0.6886477,0.0008592827,0.005489308,0.002917901,0.001570236,0.005384092,0.03298651],"genre_scores_gemma":[0.6162093,0.00005323502,0.3825083,0.00003166931,0.0004631423,0.000218034,0.0001697987,0.000113689,0.0002328136],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9230841,"threshold_uncertainty_score":0.9999791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04864185833249821,"score_gpt":0.2760078399580431,"score_spread":0.2273659816255449,"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."}}