{"id":"W4409284794","doi":"10.1109/trs.2025.3559394","title":"Classification of Radar Targets via Distribution Matching of Late-Time Resonance Parameters","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Radar Systems","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Radar; Matching (statistics); Distribution (mathematics); Resonance (particle physics); Geodesy; Geology; Remote sensing; Nuclear magnetic resonance; Computer science; Physics; Mathematics; Statistics; Mathematical analysis; Telecommunications; Atomic physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001699481,0.0004956222,0.0007659364,0.001703545,0.0002840992,0.0008765896,0.001025419,0.001004646,0.000759075],"category_scores_gemma":[0.004968745,0.0002657099,0.0004231679,0.0009132801,0.0005078529,0.00155026,0.0007918985,0.0008607546,0.0006187594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005001616,"about_ca_system_score_gemma":0.0003464773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000718663,"about_ca_topic_score_gemma":0.0005920381,"domain_scores_codex":[0.999271,0.0001899646,0.00003827198,0.0001970329,0.0001987079,0.0001050409],"domain_scores_gemma":[0.9976927,0.001028465,0.0004643497,0.0002905386,0.0004298505,0.00009413301],"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":[0.0009111015,0.0004169523,0.01482896,0.00009956422,0.00008836613,0.0002194613,0.0002341071,0.2994433,0.112339,0.009628902,0.001598194,0.560192],"study_design_scores_gemma":[0.00001089323,0.00005113565,0.002479194,0.000003489931,0.000006261642,0.0001088065,0.00002869262,0.9843026,0.008799844,0.003900326,0.0002911129,0.00001761357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09065574,0.00009023066,0.9075738,0.00009907093,0.00001718245,0.00003433111,0.00004824704,0.0005654784,0.0009159631],"genre_scores_gemma":[0.8721587,0.00009998222,0.1260317,0.00008346325,0.00004436057,0.00004083275,0.0002734148,0.000107551,0.001160032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001703545,"threshold_uncertainty_score":0.008987784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008697567486887919,"score_gpt":0.2330990258129342,"score_spread":0.2244014583260462,"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."}}