{"id":"W4319662280","doi":"10.22541/au.167597462.26786780/v1","title":"An Innovative Digital Equalizer for Wireless Communications","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Predistortion; Electronic engineering; Computer science; Amplifier; Finite impulse response; Frequency response; Nonlinear distortion; Frequency band; Adaptive equalizer; Radio frequency; Nonlinear system; Wireless; Equalization (audio); Electrical engineering; Engineering; Channel (broadcasting); Telecommunications; Bandwidth (computing); Physics","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005334887,0.0001862549,0.0002253214,0.0002395041,0.0001223208,0.00110455,0.00398459,0.0002161558,0.000003001981],"category_scores_gemma":[0.00006930091,0.0001819232,0.00007597191,0.0005186436,0.00008352444,0.0007221986,0.003215724,0.0003655852,0.00004000807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004805853,"about_ca_system_score_gemma":0.0002551422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004021648,"about_ca_topic_score_gemma":0.00003359421,"domain_scores_codex":[0.9986589,0.00009020629,0.0003759684,0.000495937,0.0001999511,0.000179045],"domain_scores_gemma":[0.9956819,0.0003107911,0.0002094681,0.003081529,0.000659958,0.00005629944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001591813,0.00008815158,0.00003754116,0.00001803706,0.00002584811,2.816855e-7,0.002450084,0.00004160734,0.00004885751,0.9757335,0.004651914,0.01690258],"study_design_scores_gemma":[0.0002512768,0.0001668518,0.0002785065,0.00008646653,0.00000642531,0.000001614961,0.0002329878,0.4261035,0.003281414,0.5329849,0.03582541,0.0007806655],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007097722,0.000008379197,0.9833397,0.004821612,0.0001646841,0.0007443311,0.000095789,0.002583754,0.007531931],"genre_scores_gemma":[0.5666542,0.00001769675,0.4287916,0.0008441934,0.0000443971,0.0007726434,0.0006118494,0.00003667788,0.002226804],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5659444,"threshold_uncertainty_score":0.9999324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1415796382059004,"score_gpt":0.4050718807453504,"score_spread":0.26349224253945,"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."}}