{"id":"W3193774407","doi":"10.1109/antem51107.2021.9519089","title":"Radar Antenna Gain Improvement Using 3D-Printed Dielectric Lens and Metamaterial-Inspired Superstrates","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Antenna and Metasurface Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Metamaterial; Lens (geology); Optics; Materials science; Dielectric; Antenna (radio); Azimuth; Radar; Optoelectronics; Computer science; Telecommunications; Physics","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":[0.00006917167,0.0001941343,0.0002648477,0.00007999671,0.0000628761,0.00004715199,0.00007604792,0.00007858019,0.00005214441],"category_scores_gemma":[0.00004423438,0.0001711834,0.00004197853,0.0002557128,0.00004654461,0.0001792594,0.00006232398,0.0001065044,0.000004601983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004667612,"about_ca_system_score_gemma":0.00001761591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002173181,"about_ca_topic_score_gemma":0.00002023446,"domain_scores_codex":[0.9991222,0.00001159592,0.0002100412,0.0002313452,0.00008502128,0.0003397881],"domain_scores_gemma":[0.9996571,0.00002138024,0.00001956078,0.000212674,0.00004999814,0.00003930605],"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":[0.000003823694,0.00001191549,0.00006541541,0.00004532552,0.00006582341,0.00002920351,0.00003145217,0.0003260003,0.9950049,0.0004073069,0.000007702481,0.004001123],"study_design_scores_gemma":[0.0003125788,0.00004253121,0.0001342823,0.00002357439,0.00004045416,0.00002447148,0.0005910033,0.04843511,0.9494803,0.0002585545,0.0004147732,0.0002423622],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.964817,0.002388071,0.0310221,0.00007118585,0.0002028999,0.0001202133,0.000008270576,0.0009104329,0.0004597946],"genre_scores_gemma":[0.9701959,0.001536828,0.02802352,0.00004154064,0.00001671991,0.000005875143,0.000006204103,0.00002994411,0.0001434081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04810911,"threshold_uncertainty_score":0.6980658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02062402024690577,"score_gpt":0.228508955491859,"score_spread":0.2078849352449532,"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."}}