{"id":"W4413120764","doi":"10.1109/tim.2025.3596985","title":"Enhanced Super-Resolution DOA Estimation via Aperture Extrapolation in RIS Technology for ITS Applications","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Optical Systems and Laser Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extrapolation; Computer science; Synthetic aperture radar; Resolution (logic); Superresolution; Estimation; Aperture (computer memory); Image resolution; Electronic engineering; Remote sensing; Artificial intelligence; Acoustics; Physics; Engineering; Mathematics; Statistics; Geology; Systems engineering","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":[0.0001506008,0.0001281427,0.0001329502,0.0004504057,0.0001279978,0.0000199954,0.00005296786,0.0001815705,0.00001360268],"category_scores_gemma":[0.000008980759,0.0001373,0.00003266632,0.0004369057,0.00002821601,0.0001263004,5.953666e-7,0.0001391491,0.000006458145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000321953,"about_ca_system_score_gemma":0.0000202008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001225095,"about_ca_topic_score_gemma":0.0001819442,"domain_scores_codex":[0.9991772,0.00001404236,0.0002977751,0.0001981564,0.0001295264,0.0001832564],"domain_scores_gemma":[0.9997063,0.00003276334,0.00002656188,0.0001189925,0.00008151317,0.00003390052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005554816,0.0001837464,0.0000292352,0.0002908374,0.00007425914,1.264954e-7,0.0001408225,0.070377,0.3122182,0.00531467,0.00004202214,0.6112735],"study_design_scores_gemma":[0.002350723,0.0001507368,0.0004240753,0.0001835001,0.0000670506,0.00000277376,0.0003292823,0.5873343,0.4015906,0.001577548,0.005707929,0.0002815358],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01998257,0.0001448371,0.9771944,0.0006803487,0.0002476921,0.001155317,0.00001635033,0.0002508826,0.000327583],"genre_scores_gemma":[0.9946502,0.00007897241,0.003417752,0.00005718802,0.000007696965,0.001727204,0.00001099086,0.00001125486,0.00003869869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9746677,"threshold_uncertainty_score":0.559893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390797284876309,"score_gpt":0.2439994232732876,"score_spread":0.2300914504245246,"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."}}