{"id":"W2057642653","doi":"10.1049/iet-map.2012.0374","title":"Antenna design exploiting adjoint sensitivity‐based geometry evolution","year":2013,"lang":"en","type":"article","venue":"IET Microwaves Antennas & Propagation","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada); McMaster University","funders":"","keywords":"Sensitivity (control systems); Antenna (radio); Computer science; Geometry; Mathematics; Topology (electrical circuits); Mathematical optimization; Algorithm; Electronic engineering; Engineering; Telecommunications; Combinatorics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004766691,0.0003629834,0.0003021405,0.0003627187,0.0001766964,0.0001971292,0.0001151211,0.0001917827,0.0001035079],"category_scores_gemma":[0.0001176137,0.0003636633,0.0001229447,0.0006064935,0.00008398746,0.0009238833,0.00002899479,0.0002606314,0.0004299482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002409878,"about_ca_system_score_gemma":0.00006252703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008216919,"about_ca_topic_score_gemma":0.000008485797,"domain_scores_codex":[0.9980931,0.0001702112,0.0005060156,0.0003955395,0.0002700928,0.0005650138],"domain_scores_gemma":[0.9989534,0.0001100928,0.0001359289,0.0003087024,0.0003579487,0.0001339058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002191356,0.00006058167,0.0002858894,0.00009431531,0.00003001626,0.00001242313,0.0001801414,0.02075514,0.974876,0.0001399691,0.0006106404,0.002932991],"study_design_scores_gemma":[0.0004435687,0.00009721336,0.003934501,0.000187049,0.00002892665,0.00003831541,0.0003585556,0.9154505,0.07875671,0.0002196025,0.00003202281,0.0004530933],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0993533,0.0003796477,0.8979336,0.0002207694,0.0002934712,0.00084613,0.000006964282,0.0007649423,0.0002011674],"genre_scores_gemma":[0.96532,0.00007063007,0.03387692,0.0001617915,0.0001777171,0.00007819274,0.00009146558,0.00009918718,0.0001241135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8961193,"threshold_uncertainty_score":0.9998815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01330150746593285,"score_gpt":0.1893031770429026,"score_spread":0.1760016695769697,"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."}}