{"id":"W4241079900","doi":"10.21203/rs.2.24042/v2","title":"Robust widely linear beamforming using estimation of extended covariance matrix and steering vector","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"China Scholarship Council","keywords":"Covariance matrix; Estimation of covariance matrices; Covariance; Beamforming; Estimation; Computer science; Matrix (chemical analysis); Mathematics; Algorithm; Statistics; Engineering; Chemistry","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":[0.00150116,0.0002064425,0.0004381189,0.0005374847,0.00014159,0.0001748466,0.0008499991,0.0002039767,0.000007712012],"category_scores_gemma":[0.001210855,0.0002257151,0.00008941125,0.000860357,0.0001502452,0.0005596096,0.001962313,0.0007323233,0.000002877648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002005034,"about_ca_system_score_gemma":0.0005179081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004312582,"about_ca_topic_score_gemma":0.000004019451,"domain_scores_codex":[0.9971555,0.0002371537,0.0006006928,0.000605602,0.001084073,0.0003170123],"domain_scores_gemma":[0.9975996,0.0003653101,0.0003753632,0.0007125788,0.0008117861,0.0001353327],"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":[0.00007940806,0.0001665905,0.0002586058,0.01110313,0.0001175734,0.0000306175,0.002780411,0.8166838,0.02453666,0.06599544,0.0001179361,0.07812983],"study_design_scores_gemma":[0.0001154972,0.0001177091,0.000698269,0.001639919,0.000008089555,0.000008155173,0.00003509983,0.957503,0.03234877,0.007325927,0.00003016091,0.000169376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02006145,0.0003633791,0.9779819,0.0002690185,0.0001490872,0.0007714582,0.00002760882,0.0002604875,0.0001156702],"genre_scores_gemma":[0.4436731,0.00004976597,0.5561672,0.000002409509,0.00003722523,0.0000291923,0.000008873294,0.00001814227,0.00001405505],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4236116,"threshold_uncertainty_score":0.9204396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1504493453039281,"score_gpt":0.4209648531069188,"score_spread":0.2705155078029906,"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."}}