{"id":"W7139946958","doi":"10.23919/cnc-usnc-ursi64444.2025.11420087","title":"Deep Learning Techniques for Lossless and Passive Metasurface Design–a Review","year":2025,"lang":"","type":"article","venue":"","topic":"Metamaterials and Metasurfaces Applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Deep learning; Lossless compression; Camouflage; Artificial neural network; Key (lock); Reflection (computer programming)","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003584495,0.0004842242,0.001257476,0.0001102742,0.0008060394,0.0004406247,0.0005074133,0.0002074684,0.001211555],"category_scores_gemma":[0.0009679893,0.0004064138,0.0001765598,0.0004882131,0.0002632382,0.0002878644,0.0002981417,0.0001640639,0.00007243051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006171201,"about_ca_system_score_gemma":0.0001803332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005289133,"about_ca_topic_score_gemma":0.000008032613,"domain_scores_codex":[0.9961779,0.0007587253,0.001150704,0.001022082,0.0002624035,0.0006282502],"domain_scores_gemma":[0.9972397,0.0008441664,0.0005667179,0.0006651609,0.0005091589,0.0001750822],"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":[0.00005844329,0.00008856476,0.00001006323,0.005896663,0.0001185408,8.480309e-7,0.00006551266,0.0000663359,0.9255481,0.01698228,0.003962295,0.04720232],"study_design_scores_gemma":[0.0002838148,0.0001265165,0.00001927593,0.001936152,0.0009991623,0.000004078318,0.00009245204,0.001202129,0.7609767,0.002568958,0.2314009,0.0003897875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005335655,0.1232368,0.8586689,0.00403897,0.0005939467,0.006335771,0.00004790288,0.0002232982,0.001518732],"genre_scores_gemma":[0.0328349,0.3006563,0.648776,0.002797852,0.00009783748,0.003139659,0.00003160601,0.00007449392,0.01159135],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2274386,"threshold_uncertainty_score":0.9998388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03520788129116278,"score_gpt":0.3290570253764972,"score_spread":0.2938491440853344,"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."}}