{"id":"W2027009076","doi":"10.1109/radar.2011.5960527","title":"Regularization for capon and APES","year":2011,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"Capon; Regularization (linguistics); Estimator; Inverse problem; Spectral density estimation; Parametric statistics; Algorithm; Quadratic equation; Mathematics; Oracle; Applied mathematics; Inverse; Singular value decomposition; Computer science; Mathematical optimization; Artificial intelligence; Mathematical analysis; Statistics; Fourier transform","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":[],"consensus_categories":[],"category_scores_codex":[0.00001187516,0.00002720009,0.00002795038,0.00001543123,0.00001083076,0.00000463688,0.00001451473,0.00001935135,0.00001032057],"category_scores_gemma":[0.000002580991,0.00002452424,0.000006161645,0.00001319117,0.000004991107,0.0000289824,0.000003948954,0.00000949861,7.93743e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001960936,"about_ca_system_score_gemma":5.719485e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005842161,"about_ca_topic_score_gemma":0.000002721208,"domain_scores_codex":[0.9998851,0.000001166991,0.00002886757,0.00003394419,0.00001179061,0.00003909144],"domain_scores_gemma":[0.999929,0.000004691637,0.000002917471,0.00004397898,0.00001021629,0.000009266587],"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":[0.00004613962,0.00004858128,0.001469398,0.0001261697,0.0001266683,0.00000548005,0.002718264,0.0003876206,0.3933165,0.3471276,0.05706934,0.1975583],"study_design_scores_gemma":[0.0001043758,0.00003323498,0.001541878,0.00001409949,0.000009742078,0.00000367896,0.00002603831,0.07140591,0.8896999,0.03111884,0.005925618,0.0001166904],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08996225,0.0001440903,0.837834,0.00001470494,0.00008847346,0.0001543455,8.624778e-7,0.001059709,0.07074156],"genre_scores_gemma":[0.9637829,0.00001506448,0.03595876,0.00002104535,0.00001436299,0.000004782715,0.00000133593,0.000007157965,0.000194611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8738206,"threshold_uncertainty_score":0.1000069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03038338409288113,"score_gpt":0.1924715593540316,"score_spread":0.1620881752611505,"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."}}