{"id":"W1989097365","doi":"10.1109/icassp.2002.5745285","title":"On uniqueness of direction of arrival estimates using RAnk Reduction Estimator (RARE)","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Acoustics Speech and Signal Processing","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Uniqueness; Identifiability; Direction of arrival; Estimator; Rank (graph theory); Reduction (mathematics); Equivalence (formal languages); Algorithm; SIGNAL (programming language); Mathematics; Computer science; Manifold (fluid mechanics); Applied mathematics; Statistics; Combinatorics; Mathematical analysis; Telecommunications; Engineering; Discrete mathematics; Geometry","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":[0.0002325219,0.0001800167,0.0002734946,0.0004036862,0.00009337523,0.0000949887,0.0003701663,0.00009547503,0.00005147399],"category_scores_gemma":[0.0001496534,0.0001780227,0.00005189431,0.0002910354,0.0001713474,0.0005255212,0.00004239469,0.000155502,0.000001314016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006858991,"about_ca_system_score_gemma":0.00009425599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004284882,"about_ca_topic_score_gemma":4.282181e-7,"domain_scores_codex":[0.9984307,0.00004125809,0.0005148897,0.000307672,0.000567471,0.0001380542],"domain_scores_gemma":[0.9982074,0.0001275478,0.0005639397,0.0001629518,0.0008776608,0.00006051255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009679954,0.0003270858,0.0001314135,0.0002473011,0.00004233724,0.000006221391,0.0003455595,0.01472701,0.7830267,0.02676689,0.00006457437,0.1742181],"study_design_scores_gemma":[0.0001502365,0.000158241,0.00008698329,0.0005238743,0.00001306299,0.00003352487,0.00002177416,0.6330253,0.3517239,0.01415622,0.000001559821,0.0001053477],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1918469,0.0000289189,0.8050703,0.00008307871,0.0003344768,0.0001279309,0.00001129319,0.0001030571,0.002394001],"genre_scores_gemma":[0.8663542,0.00003329935,0.1335094,0.00001249747,0.00003943314,0.000004909981,0.000002170744,0.00001087548,0.0000331942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6745073,"threshold_uncertainty_score":0.7259553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05940025821180401,"score_gpt":0.3168193453099101,"score_spread":0.2574190870981061,"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."}}