{"id":"W2160231548","doi":"10.1109/vetec.1994.345126","title":"Adaptive equalization for a multipath fading environment with interference and noise","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Multipath propagation; Computer science; Additive white Gaussian noise; Gaussian noise; Adjacent-channel interference; Fading; Impulse noise; Bandwidth (computing); Bit error rate; Electronic engineering; Channel (broadcasting); Noise (video); Interference (communication); Algorithm; Telecommunications; Engineering","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":[0.0002460505,0.0004395435,0.0002346577,0.0002991616,0.0001778828,0.0002468515,0.0002878917,0.0004128973,0.0005784894],"category_scores_gemma":[0.001367881,0.0001366041,0.0002454364,0.0002924055,0.0003248816,0.000552122,0.0002422188,0.0002798783,0.0001976792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002950347,"about_ca_system_score_gemma":0.0002846593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002410077,"about_ca_topic_score_gemma":0.00227227,"domain_scores_codex":[0.9997533,0.00006907219,0.000009608293,0.00003488854,0.0000984982,0.0000346822],"domain_scores_gemma":[0.999569,0.0002917258,0.00003916178,0.00002551798,0.00006873035,0.000005769685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00029108,0.0001001657,0.002127358,0.0001126518,0.0001064712,0.0002236503,0.0001252839,0.7657359,0.06663746,0.007671221,0.0006153552,0.1562534],"study_design_scores_gemma":[0.00001314582,0.0001528013,0.00110573,0.000005295597,0.00002634761,0.0001181104,0.00002169417,0.9831315,0.0133706,0.001318413,0.0007227667,0.00001355969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2205192,0.000438592,0.7749381,0.0001153679,0.00005249126,0.00002482547,0.00003298456,0.0003846837,0.00349386],"genre_scores_gemma":[0.9375678,0.0005742816,0.05890074,0.000050576,0.00006130719,0.00002563734,0.0000452679,0.00003537545,0.002738988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002410077,"threshold_uncertainty_score":0.004792035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0331571286756334,"score_gpt":0.222632362621978,"score_spread":0.1894752339463446,"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."}}