{"id":"W2102871601","doi":"10.1109/aps.2009.5171463","title":"Single-port direction of arrival estimation using adaptive null-forming","year":2009,"lang":"en","type":"article","venue":"Digest - IEEE Antennas and Propagation Society. International Symposium","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Direction of arrival; Multiple signal classification; Null (SQL); Computer science; Bandwidth (computing); Port (circuit theory); Electronic engineering; Algorithm; Telecommunications; Antenna (radio); Engineering; Data mining","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":[0.0004110911,0.0001913785,0.0002433245,0.000136211,0.0001526576,0.00011719,0.0003220622,0.0001127017,0.000005894779],"category_scores_gemma":[0.00005339524,0.0001931093,0.0001554845,0.0004006022,0.0001054272,0.001638804,0.00005201211,0.0001218549,0.000001603434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001505533,"about_ca_system_score_gemma":0.00006776385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008784508,"about_ca_topic_score_gemma":0.000001780498,"domain_scores_codex":[0.9982553,0.00004608941,0.0006183655,0.0003569779,0.0005514625,0.0001717939],"domain_scores_gemma":[0.9982947,0.0000730377,0.0006580402,0.0002113365,0.0006995654,0.00006332377],"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":[0.00006703347,0.0004555986,0.001684645,0.00006543453,0.0001356018,0.000002810593,0.003374593,0.003092095,0.8764268,0.03357401,0.000190105,0.08093125],"study_design_scores_gemma":[0.0003156819,0.0003290156,0.003172901,0.0002279693,0.00002590071,0.00004831747,0.0001011815,0.6118045,0.3800418,0.003489651,0.0001968545,0.0002461407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1567468,0.00004363977,0.8383557,0.0007518878,0.0006936091,0.0003249821,0.0000107954,0.0002132838,0.002859355],"genre_scores_gemma":[0.9200745,0.00007820687,0.0795228,0.0001130269,0.00009072173,0.00001086704,0.00001940953,0.00001107644,0.00007934402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7633277,"threshold_uncertainty_score":0.787477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02329416445402963,"score_gpt":0.2735889685433817,"score_spread":0.250294804089352,"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."}}