{"id":"W4402979186","doi":"10.1109/ap-s/inc-usnc-ursi52054.2024.10686963","title":"Fabry Perot Array Antenna Design Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; École de Technologie Supérieure","funders":"","keywords":"Computer science; Antenna (radio); Antenna array; Fabry–Pérot interferometer; Electronic engineering; Electrical engineering; Engineering; Telecommunications; Optics; 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":[0.0001275385,0.0001186956,0.00009349696,0.0001006781,0.00005726403,0.00009826846,0.0000544277,0.00005577073,0.0003879892],"category_scores_gemma":[0.00001288279,0.0001035133,0.00004126288,0.0002064556,0.00001279996,0.0001753303,0.000007279962,0.0001809368,0.0001202799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004270181,"about_ca_system_score_gemma":0.00001183077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000169487,"about_ca_topic_score_gemma":0.000001243215,"domain_scores_codex":[0.9994674,0.00002531018,0.0001205541,0.0001293103,0.00008160141,0.0001758715],"domain_scores_gemma":[0.9998289,0.00003393653,0.00000568033,0.00007096375,0.00001601463,0.00004446681],"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":[0.000008947164,0.000008827108,0.0003345954,0.0001330394,0.00007354347,0.00004831087,0.000586177,0.6825614,0.3059059,0.0007580756,0.0004927051,0.009088431],"study_design_scores_gemma":[0.00005223501,0.00001483459,0.000008366862,0.00005366481,0.00001378998,0.00002109404,0.00004860946,0.9965704,0.001990518,0.0000526808,0.001032397,0.0001414023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007729268,0.001312881,0.9936353,0.00003046462,0.0002695559,0.00008741227,9.25372e-7,0.001187322,0.002703218],"genre_scores_gemma":[0.936265,0.0001889761,0.0614189,0.00003552365,0.00009152657,0.000003290177,0.000006867486,0.0000540488,0.001935881],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.935492,"threshold_uncertainty_score":0.4248209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02692148100588859,"score_gpt":0.2221605413731802,"score_spread":0.1952390603672916,"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."}}