{"id":"W4383112877","doi":"10.1109/ojap.2023.3292108","title":"A Deep Learning-Based Approach to Design Metasurfaces From Desired Far-Field Specifications","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Antennas and Propagation","topic":"Metamaterials and Metasurfaces Applications","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; CMC Microsystems","keywords":"Near and far field; Computer science; Field (mathematics); Artificial intelligence; Systems engineering; Engineering; Physics; Optics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002583991,0.0001376008,0.000350325,0.0001323027,0.000304942,0.0005242951,0.0004652425,0.00005574885,0.0001376673],"category_scores_gemma":[0.0002663177,0.0001047674,0.00005098114,0.0004077343,0.000036491,0.00037815,0.00004937243,0.0001042146,0.0001254615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001786802,"about_ca_system_score_gemma":0.000085173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008118392,"about_ca_topic_score_gemma":0.000006361834,"domain_scores_codex":[0.9982828,0.0003894149,0.0005856213,0.000258285,0.0002909568,0.0001929584],"domain_scores_gemma":[0.9985769,0.0002604262,0.0005074554,0.0002042688,0.0002926921,0.0001581941],"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":[0.0001779609,0.00006033643,0.00005961974,0.00001110048,0.00002010668,0.000002279423,0.000365937,0.022943,0.9731907,0.0001303633,0.0006840749,0.00235448],"study_design_scores_gemma":[0.0007866579,0.0004768168,0.00289352,0.00007971325,0.0001404013,0.00001538939,0.0006913295,0.04508512,0.9423638,0.001164972,0.006025818,0.000276458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5065238,0.0001784639,0.4915527,0.00082736,0.0002378927,0.0005213509,0.00001062593,0.00002307947,0.0001246981],"genre_scores_gemma":[0.8706621,0.0002021798,0.1286321,0.0001852415,0.00007938856,0.00005364431,0.00001568487,0.00001850165,0.0001511727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3641383,"threshold_uncertainty_score":0.505579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1314847097462162,"score_gpt":0.3034517795022768,"score_spread":0.1719670697560605,"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."}}