{"id":"W2344848012","doi":"10.1109/joe.2015.2431740","title":"Improving Statistical Robustness of Matched-Field Source Localization via General-Rank Covariance Matrix Matching","year":2015,"lang":"en","type":"article","venue":"IEEE Journal of Oceanic Engineering","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Covariance matrix; Robustness (evolution); Estimator; Algorithm; Covariance; Noise power; Computer science; Minimum-variance unbiased estimator; Estimation of covariance matrices; Mathematics; Statistics; Power (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":[0.001798718,0.0009431334,0.0006016768,0.0007842398,0.0002897719,0.0006145671,0.0009443221,0.000843577,0.001551493],"category_scores_gemma":[0.01076331,0.0003517034,0.0004716233,0.0008273117,0.0005877779,0.001798839,0.001211374,0.0006587122,0.0008206923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003522418,"about_ca_system_score_gemma":0.0009027055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00176676,"about_ca_topic_score_gemma":0.001547339,"domain_scores_codex":[0.9985672,0.0004426466,0.00007307449,0.000250898,0.0005486905,0.0001175095],"domain_scores_gemma":[0.9972535,0.001345947,0.0002413051,0.0004751082,0.0006312303,0.00005282959],"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":[0.0005256867,0.0001467862,0.0021142,0.0001837249,0.000124435,0.000203011,0.0001588368,0.563098,0.0753947,0.02027185,0.00245586,0.3353229],"study_design_scores_gemma":[0.00001874681,0.00006015238,0.000433984,0.000007076379,0.0000107855,0.00008203739,0.00001323365,0.9772714,0.01718428,0.003958668,0.0009367468,0.00002278107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01208289,0.00007103117,0.9865956,0.00005542253,0.00001705838,0.00001791739,0.00002908748,0.0004487421,0.0006823202],"genre_scores_gemma":[0.5044638,0.0002770576,0.4923071,0.0001742382,0.00006279421,0.00008485684,0.0003121231,0.0001679624,0.002149984],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001798718,"threshold_uncertainty_score":0.009512663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01062385610054938,"score_gpt":0.2405481833512241,"score_spread":0.2299243272506747,"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."}}