{"id":"W4296559029","doi":"10.1109/ap-s/usnc-ursi47032.2022.9887064","title":"Power Pattern to Planar Dipole Array Synthesis Using a Text-to-Image Transformer Based Model","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Planar; Dipole; Computer science; Transformer; Perpendicular; Planar array; Artificial neural network; Power (physics); Topology (electrical circuits); Artificial intelligence; Electrical engineering; Physics; Engineering; Voltage; Mathematics; Telecommunications; Computer graphics (images); Geometry","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":[0.0006584203,0.0002826977,0.000241227,0.0005284554,0.0006456324,0.0002648929,0.0005589537,0.0000494333,0.00006942276],"category_scores_gemma":[0.0001589459,0.0002786648,0.00006201767,0.0006347643,0.0002785012,0.0004240971,0.00005759324,0.0003357269,0.00001043908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000381882,"about_ca_system_score_gemma":0.00005142159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002708125,"about_ca_topic_score_gemma":0.000005318229,"domain_scores_codex":[0.9976853,0.00003767727,0.0003419541,0.0006288383,0.0008084101,0.0004978827],"domain_scores_gemma":[0.9992423,0.0001007971,0.00007354871,0.0002355626,0.0001174271,0.0002303624],"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":[0.00006529946,0.00006297285,0.0003764897,0.00002224544,0.00002079352,0.000009583978,0.0007193898,0.1895221,0.8042205,0.000342376,0.0002722335,0.004366011],"study_design_scores_gemma":[0.0003047537,0.0001630518,0.0004232295,0.0001546074,0.00002060983,0.00004072877,0.000588801,0.8977352,0.0988443,0.000178324,0.001091767,0.0004545627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6416938,0.0000709112,0.3391405,0.01282937,0.001232532,0.0006347997,0.0001424259,0.0004824921,0.003773201],"genre_scores_gemma":[0.9851121,0.00002588348,0.01363973,0.0008939766,0.00004594141,0.0001039273,0.000006359064,0.0000420401,0.0001300502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7082132,"threshold_uncertainty_score":0.9999666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409103313148461,"score_gpt":0.2547606062146366,"score_spread":0.240669573083152,"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."}}