{"id":"W4403726390","doi":"10.1109/iws61525.2024.10713782","title":"An Efficient DNN/ML Training Approach for Designing Metasurfaces","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Antenna and Metasurface Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Training (meteorology); Computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002728858,0.0004391063,0.0002281536,0.0002507393,0.0001407542,0.000337471,0.0007239049,0.0005303126,0.00160293],"category_scores_gemma":[0.0006405016,0.000257457,0.0002662412,0.0002299075,0.0002716155,0.0006206867,0.0004744634,0.0007354484,0.0004337082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005041893,"about_ca_system_score_gemma":0.0004054437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001083561,"about_ca_topic_score_gemma":0.002475635,"domain_scores_codex":[0.9999297,0.00001349353,0.000004256236,0.00001540286,0.00002852417,0.0000085989],"domain_scores_gemma":[0.9998658,0.00005181761,0.00001488619,0.00002035791,0.00003937481,0.000007744462],"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":[0.00006615724,0.0000468664,0.0005127394,0.0001179132,0.00002531821,0.00007293394,0.00004092301,0.8137892,0.03994191,0.01390367,0.001377724,0.1301047],"study_design_scores_gemma":[0.000002105877,0.000009529084,0.00001959123,0.000002640918,0.000001501444,0.000007722004,0.000002480108,0.9948019,0.003376112,0.001343741,0.0004314433,0.000001328343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01508794,0.0001114933,0.9810411,0.000146912,0.00001865302,0.00002510443,0.00005927512,0.0005182243,0.00299133],"genre_scores_gemma":[0.4346063,0.0001759242,0.56183,0.0001745197,0.00001883708,0.0001245844,0.0002336298,0.0001212915,0.002714862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00160293,"threshold_uncertainty_score":0.005362272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04741293349870225,"score_gpt":0.276033646287348,"score_spread":0.2286207127886458,"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."}}