{"id":"W2143939044","doi":"10.1109/twc.2008.060902","title":"Finger Replacement Method for Rake Receivers in the Soft Handover Region","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Rake receiver; Computer science; Rake; Handover; Base station; Overhead (engineering); Soft handover; Path (computing); Signal-to-noise ratio (imaging); Algorithm; Telecommunications; Channel (broadcasting); Real-time computing; Computer network; Fading; 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":[0.001319408,0.0005725714,0.0008893243,0.0004786879,0.0004814606,0.0008666798,0.001230727,0.0009594073,0.001146179],"category_scores_gemma":[0.003587713,0.0002910491,0.0005514906,0.0004654042,0.0006552181,0.001282138,0.0006548922,0.0008223143,0.0006344945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004159005,"about_ca_system_score_gemma":0.0003712313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003570843,"about_ca_topic_score_gemma":0.0003809902,"domain_scores_codex":[0.998902,0.0002955477,0.00005330972,0.0001258578,0.0005023572,0.0001208297],"domain_scores_gemma":[0.9975951,0.001012073,0.0003050551,0.0006504075,0.0003679705,0.00006940201],"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":[0.001509237,0.0002274679,0.003190608,0.0003053559,0.000168349,0.0007124901,0.0004413901,0.1876259,0.2921933,0.03881967,0.001444053,0.4733621],"study_design_scores_gemma":[0.00004504067,0.0005301228,0.000806259,0.00001950333,0.00007188672,0.001182018,0.00003865431,0.9380071,0.05139282,0.003937946,0.003899332,0.00006930374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.018009,0.0004140038,0.9805328,0.00003896831,0.0000288356,0.00002796176,0.00001067824,0.0002962087,0.0006415329],"genre_scores_gemma":[0.5633794,0.0003651838,0.4342369,0.00007899274,0.00008815478,0.00007154122,0.0000280844,0.0000466561,0.001704962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001319408,"threshold_uncertainty_score":0.006977797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08729465346552,"score_gpt":0.3380609026037336,"score_spread":0.2507662491382136,"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."}}