{"id":"W2036633357","doi":"10.1155/2010/623540","title":"Advanced Equalization Techniques for Wireless Communications","year":2011,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Atlanta; Telecommunications; Computer science; Wireless; Electrical engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004675324,0.0002614977,0.0002983546,0.0003747486,0.0003073821,0.00006411916,0.0009798141,0.0001028366,0.00001852735],"category_scores_gemma":[0.00005515746,0.0002646955,0.00007532655,0.0004722413,0.000138347,0.001756316,0.0000670419,0.0006206491,0.000003434518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002231225,"about_ca_system_score_gemma":0.00003912019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.230406e-7,"about_ca_topic_score_gemma":0.000006920644,"domain_scores_codex":[0.9984288,0.00009437738,0.0007152831,0.0001962335,0.0002180412,0.0003472149],"domain_scores_gemma":[0.9986798,0.0002007348,0.0003010708,0.000493841,0.0002337864,0.0000908121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007848098,0.0001101832,0.0001519415,0.0001186089,0.000006686917,0.000003172921,0.0006460978,0.003102181,0.007647344,0.004836919,0.00002793041,0.9832705],"study_design_scores_gemma":[0.002257684,0.001023093,0.000370901,0.004736441,0.00004566542,0.0001743255,0.001725358,0.1721649,0.5636503,0.139821,0.1118688,0.002161611],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002526408,0.009791317,0.9738336,0.00006518384,0.0001025287,0.0004891249,0.000006523982,0.0008623486,0.01232298],"genre_scores_gemma":[0.7691674,0.007632588,0.2227605,0.0001013399,0.00005278999,0.0001836861,0.000009438894,0.00007621471,0.00001604448],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9811088,"threshold_uncertainty_score":0.9999805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04527208174357795,"score_gpt":0.3291665287974841,"score_spread":0.2838944470539062,"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."}}