{"id":"W2940751552","doi":"10.1109/access.2019.2912119","title":"Optimum Wideband High Gain Analog Beamforming Network for 5G Applications","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Beamforming; Extremely high frequency; Wideband; Monolithic microwave integrated circuit; Broadband; Electronic engineering; Antenna array; Computer science; Antenna (radio); Electrical engineering; Telecommunications; Engineering; Bandwidth (computing)","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":[0.0001769964,0.0004041527,0.000130565,0.0002438635,0.0002047426,0.0004311395,0.0002381486,0.0002895316,0.00338731],"category_scores_gemma":[0.0002366982,0.0001235554,0.0001197054,0.0003577331,0.0001342765,0.0005175965,0.0001925142,0.0001495332,0.000976919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005077696,"about_ca_system_score_gemma":0.0002393434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006598312,"about_ca_topic_score_gemma":0.002482755,"domain_scores_codex":[0.9997362,0.00007251176,0.000009030118,0.00004791848,0.00009934196,0.00003484933],"domain_scores_gemma":[0.9998946,0.0000253785,0.00002501584,0.000005576128,0.00004256255,0.000006786797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005564615,0.00006757952,0.002443041,0.0001625787,0.00004863097,0.0001898151,0.00009665762,0.03173673,0.656935,0.02643614,0.005218678,0.2761086],"study_design_scores_gemma":[0.0001159691,0.001095028,0.00514066,0.00009004729,0.000108035,0.001124188,0.0001773328,0.5463724,0.3771843,0.00684928,0.06164341,0.00009934804],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09055791,0.0008370542,0.8783267,0.0006068625,0.00009574627,0.0000620846,0.0002200312,0.0006833248,0.02861037],"genre_scores_gemma":[0.8210666,0.0005758709,0.1696686,0.0002278588,0.00007722611,0.00006745977,0.0002895241,0.00003365642,0.007993163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00338731,"threshold_uncertainty_score":0.01133168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01180819990197824,"score_gpt":0.2434539640184031,"score_spread":0.2316457641164249,"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."}}