{"id":"W2544713916","doi":"10.1109/lpt.2016.2623322","title":"Robust Frame and Frequency Synchronization Based on Alamouti Coding for RGI-CO-OFDM","year":2016,"lang":"en","type":"article","venue":"IEEE Photonics Technology Letters","topic":"Optical Network Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Atlantic Canada Opportunities Agency","keywords":"Orthogonal frequency-division multiplexing; Computer science; Carrier frequency offset; Frequency offset; Algorithm; Electronic engineering; Polarization-division multiplexing; Multiplexing; Coding (social sciences); Frame synchronization; Orthogonal polarization spectral imaging; Guard interval; Synchronization (alternating current); Telecommunications; Channel (broadcasting); Mathematics; Engineering; Signal processing; Physics; Laser; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004689544,0.0004991364,0.0004126555,0.0005811453,0.000489961,0.0004128935,0.0006801112,0.0004419475,0.0007072267],"category_scores_gemma":[0.001357706,0.0001922662,0.0002443465,0.0005031531,0.0005003964,0.0005769344,0.0004453914,0.0006397012,0.0003152523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004628839,"about_ca_system_score_gemma":0.001179626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002057689,"about_ca_topic_score_gemma":0.002551323,"domain_scores_codex":[0.9995365,0.00009588367,0.00002492191,0.00007366668,0.0002208042,0.00004827582],"domain_scores_gemma":[0.9995604,0.0001318474,0.0001079857,0.00007879328,0.0001045526,0.00001646605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005012301,0.000138491,0.00186303,0.0001555429,0.0000878968,0.0002823468,0.0003435649,0.227364,0.1294279,0.06351813,0.002715971,0.5736019],"study_design_scores_gemma":[0.0000437372,0.0001369313,0.0004153915,0.00001807194,0.00002296639,0.0001690992,0.00001830576,0.9554342,0.0363782,0.003859095,0.003464705,0.00003932815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01331872,0.0001873568,0.9852301,0.00006222864,0.00004462073,0.00003543886,0.00002537517,0.0002818931,0.0008142683],"genre_scores_gemma":[0.2389987,0.0002048246,0.7592271,0.00005571728,0.0000725592,0.0001257641,0.0001196189,0.00003502488,0.001160782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002057689,"threshold_uncertainty_score":0.004091382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01194159025565078,"score_gpt":0.2088163888835471,"score_spread":0.1968747986278963,"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."}}