{"id":"W4205303671","doi":"10.1364/acpc.2021.t4a.40","title":"Low-Complexity Single-Stage Frequency-Domain Equalizer","year":2021,"lang":"en","type":"article","venue":"Asia Communications and Photonics Conference 2021","topic":"Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Equalizer; Computer science; Adaptive equalizer; Frequency domain; Degradation (telecommunications); Single stage; Computational complexity theory; Electronic engineering; Algorithm; Telecommunications; Engineering; Channel (broadcasting)","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":[0.000290014,0.0005648564,0.0004878346,0.0004485086,0.0003148967,0.0006285728,0.001105426,0.0006780322,0.00501309],"category_scores_gemma":[0.0004640146,0.0002905304,0.0003980258,0.0003625144,0.0002005009,0.000900451,0.0004506347,0.0007047672,0.001404359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003404767,"about_ca_system_score_gemma":0.0005954596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000758555,"about_ca_topic_score_gemma":0.001893996,"domain_scores_codex":[0.9996359,0.00004095402,0.00002195649,0.00008394808,0.0001499281,0.00006727975],"domain_scores_gemma":[0.9997792,0.00005612344,0.00002023452,0.00003869559,0.00009344648,0.00001232408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005388768,0.0001823206,0.00146317,0.0002023484,0.0001331944,0.0002100023,0.00005145307,0.0192714,0.5864929,0.01001598,0.003740215,0.3776981],"study_design_scores_gemma":[0.000173332,0.0005432109,0.003061632,0.00002188801,0.0001489728,0.001326655,0.00003319439,0.4338339,0.5373265,0.00173949,0.02170283,0.00008839874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03679135,0.0004536364,0.9579961,0.0002301363,0.0001939795,0.00007624564,0.00009183063,0.0008902621,0.003276352],"genre_scores_gemma":[0.3901409,0.0003334424,0.5969596,0.0003391822,0.0002208714,0.00009245359,0.0002947067,0.0000676087,0.01155127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00501309,"threshold_uncertainty_score":0.01677042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06333837527473618,"score_gpt":0.2669617151630281,"score_spread":0.203623339888292,"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."}}