{"id":"W2372645900","doi":"","title":"An improved nonlinear compensation algorithm in broadband PCS channel","year":2006,"lang":"en","type":"article","venue":"Journal of Anshan University of Science and Technology","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Channel (broadcasting); Algorithm; Computer science; Bit error rate; Compensation (psychology); Distortion (music); Broadband; Transmission (telecommunications); Nonlinear distortion; Nonlinear system; Telecommunications; Bandwidth (computing); Amplifier","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":[0.000818581,0.00004798473,0.0001350886,0.001073675,0.0001694812,0.00003360839,0.001598806,0.00006954499,0.000001153595],"category_scores_gemma":[0.00002133686,0.00004921512,0.00001508502,0.001801103,0.001152158,0.0009505553,0.0002561971,0.0002178693,4.002285e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006173121,"about_ca_system_score_gemma":0.0002336483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000121159,"about_ca_topic_score_gemma":0.0001227534,"domain_scores_codex":[0.9992352,0.00003154354,0.0001568428,0.0001365608,0.0002749322,0.0001649633],"domain_scores_gemma":[0.998824,0.00002899912,0.0001995283,0.0002672109,0.000630867,0.00004940923],"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":[0.00007416028,0.001109953,0.01529188,0.0000297592,0.00002178147,0.0001889168,0.002466381,0.001464497,0.1877324,0.0467074,0.0002337032,0.7446792],"study_design_scores_gemma":[0.001080532,0.0006620542,0.0181241,0.00004744518,0.000003501161,0.0001119488,0.00210622,0.9644679,0.007525932,0.005202646,0.0005435729,0.0001240967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8494275,0.0001649007,0.1470337,0.002964245,0.00005120358,0.00007884191,0.000001160364,0.00002335274,0.0002550857],"genre_scores_gemma":[0.9368112,0.0001295902,0.06302374,0.000008612987,0.00001087216,2.881664e-8,2.863664e-7,0.000001254117,0.00001442051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9630035,"threshold_uncertainty_score":0.4245174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008985560932943245,"score_gpt":0.2309470977118704,"score_spread":0.2219615367789272,"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."}}