{"id":"W2163128415","doi":"10.1109/twc.2006.1687731","title":"Unified analysis of generalized selection combining with normalized threshold test per branch","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Fading; Robustness (evolution); Maximal-ratio combining; Mathematics; Algorithm; Diversity combining; Selection (genetic algorithm); Moment-generating function; Computer science; Statistics; Probability density function; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001674646,0.0003155717,0.0005529171,0.000912295,0.0004583991,0.00004414071,0.000980148,0.000160079,0.00008041369],"category_scores_gemma":[0.000002687219,0.0003308196,0.0002200111,0.002593213,0.0002805497,0.0003169972,0.000008385475,0.000572723,0.000008551134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001653176,"about_ca_system_score_gemma":0.00004149587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003649823,"about_ca_topic_score_gemma":0.002200658,"domain_scores_codex":[0.9983695,0.0001184236,0.0006902785,0.0002436143,0.0002876434,0.0002905951],"domain_scores_gemma":[0.9966833,0.0004803342,0.0001874334,0.002294671,0.0002849452,0.00006930422],"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":[0.00004364457,0.0006474492,0.001211359,0.00003289033,0.000680625,2.907679e-7,0.0002094409,0.905194,0.08350374,0.005143399,0.00006475673,0.003268352],"study_design_scores_gemma":[0.001360841,0.0001368458,0.003589067,0.0001074517,0.001179033,0.000009448703,0.00008870862,0.7526168,0.2390691,0.0003494023,0.0007949767,0.0006983216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1668677,0.0002111939,0.8284027,0.000133431,0.0000404643,0.0003788076,0.000100324,0.001173658,0.002691667],"genre_scores_gemma":[0.971357,0.0014224,0.0264415,0.00003029891,0.000008059854,0.0003748909,0.0001373304,0.00008292899,0.0001455293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8044893,"threshold_uncertainty_score":0.9999144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621334725604627,"score_gpt":0.2541037487761018,"score_spread":0.2378904015200556,"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."}}