{"id":"W2157291908","doi":"10.1109/tvt.2006.878725","title":"Optimized Delay Diversity for Suboptimum Equalization","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Equalization (audio); Fading; Computer science; Bit error rate; Transmitter; Transmission (telecommunications); Channel (broadcasting); Diversity combining; GSM; Channel state information; Wireless; Electronic engineering; Diversity scheme; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007617893,0.0001665204,0.0001913506,0.0004126928,0.0003077191,0.00001088715,0.000320428,0.0003124178,0.00001755241],"category_scores_gemma":[0.000003598444,0.0001942461,0.00009864275,0.0004293479,0.00009116004,0.0001225493,0.000004971542,0.0002459199,0.00001366125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001372454,"about_ca_system_score_gemma":0.000007220102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001384171,"about_ca_topic_score_gemma":0.00001997348,"domain_scores_codex":[0.9992369,0.00001782957,0.0002267854,0.0001909141,0.00009884984,0.0002287417],"domain_scores_gemma":[0.9992796,0.00005743713,0.00004117678,0.0005060946,0.00009162118,0.00002405481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001873846,0.00007644585,0.000006112076,0.00001972869,0.00003405234,0.000001931397,0.00001570315,0.9691693,0.01469513,0.003477313,0.0001953298,0.01229024],"study_design_scores_gemma":[0.000908641,0.00008993562,0.000006873523,0.00002378608,0.00004939816,0.00001176629,0.0000329402,0.3321342,0.6538622,0.00863549,0.003906423,0.0003382954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0187782,0.0001373203,0.9766249,0.00019911,0.0001329185,0.0004346931,0.00002795937,0.003464109,0.0002007928],"genre_scores_gemma":[0.9008152,0.0001508541,0.0985911,0.00002397494,0.00001033432,0.0002663604,0.00001835542,0.00004227668,0.00008156453],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.882037,"threshold_uncertainty_score":0.7921125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099625557959487,"score_gpt":0.2303845076338987,"score_spread":0.2193882520543038,"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."}}