{"id":"W4242657727","doi":"10.21203/rs.2.24042/v1","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":"","keywords":"Covariance matrix; Beamforming; Estimation of covariance matrices; Covariance; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001175334,0.001560144,0.001200581,0.0007803448,0.0003272575,0.001148578,0.0008268413,0.001109681,0.001841016],"category_scores_gemma":[0.007343532,0.0008746377,0.0009096703,0.001463892,0.0006719685,0.002229105,0.001476499,0.001976268,0.001328971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003949917,"about_ca_system_score_gemma":0.00101113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001240926,"about_ca_topic_score_gemma":0.001876723,"domain_scores_codex":[0.9988081,0.0003949051,0.00006942546,0.0002737468,0.0003770681,0.00007669053],"domain_scores_gemma":[0.9981155,0.0008753766,0.0002425269,0.0003017628,0.0004143466,0.00005055232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005105037,0.00007516357,0.001093509,0.0003117719,0.0003114043,0.0001690743,0.0001820616,0.3608793,0.09854031,0.06595245,0.004595096,0.4673793],"study_design_scores_gemma":[0.00003576006,0.00007120777,0.0005253845,0.00004031023,0.00004665908,0.0002135285,0.00002825012,0.9536281,0.01448151,0.02693127,0.00393983,0.0000581584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001418105,0.000146928,0.9978771,0.00005856031,0.00002533461,0.000004984291,0.0000238266,0.0001493965,0.0002958135],"genre_scores_gemma":[0.1206154,0.0009916142,0.8737199,0.0001539906,0.0001544739,0.00009377873,0.0004129185,0.0002136729,0.003644292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001841016,"threshold_uncertainty_score":0.006215811,"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."}}