{"id":"W2141560854","doi":"10.1002/wcm.1150","title":"Antenna subset selection at multi‐antenna relay with adaptive modulation","year":2011,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Wonkwang University","keywords":"Computer science; Relay; Antenna (radio); Spectral efficiency; Transmission (telecommunications); Link adaptation; Selection (genetic algorithm); Telecommunications; Hop (telecommunications); Computer network; Diversity gain; Antenna diversity; Electronic engineering; Fading; Decoding methods; 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004133505,0.000219087,0.0002319395,0.0001469372,0.001381461,0.0001094534,0.001296108,0.00007453684,0.000009660632],"category_scores_gemma":[0.00001332376,0.0002002685,0.00004704253,0.0006781979,0.0002061213,0.0004514847,0.001673979,0.0003211478,0.00001288995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026058,"about_ca_system_score_gemma":0.00004755002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005333201,"about_ca_topic_score_gemma":0.0003473059,"domain_scores_codex":[0.9984345,0.0003487768,0.0003607553,0.0004186252,0.0001474816,0.0002898441],"domain_scores_gemma":[0.9974333,0.0002201942,0.0002501342,0.001618116,0.0003660464,0.00011218],"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":[0.0001422767,0.001356942,0.04994025,0.00004419249,0.0002885165,0.000008645917,0.05126537,0.001916971,0.0211825,0.1462475,0.0002243838,0.7273824],"study_design_scores_gemma":[0.0004677463,0.0001729584,0.0176767,0.0001105412,0.00001089421,0.00004060854,0.0002802935,0.9794884,0.0001851725,0.00004091079,0.001253436,0.0002722975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2059812,0.001589437,0.7907426,0.0001457733,0.00006600004,0.0004212736,0.000001982052,0.0002904304,0.000761295],"genre_scores_gemma":[0.8573485,0.002052197,0.140253,0.0001095627,0.0000153743,0.00006489208,0.00001655456,0.00001851562,0.0001213547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9775715,"threshold_uncertainty_score":0.9999186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06785384826491203,"score_gpt":0.2741186257291299,"score_spread":0.2062647774642179,"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."}}