{"id":"W3160931642","doi":"10.1016/j.bspc.2021.103124","title":"Subspace based Multiple Constrained Minimum Variance (SMCMV) beamformers","year":2021,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Magnetoencephalography; Computer science; Subspace topology; Noise (video); Covariance; Variance (accounting); Algorithm; Pattern recognition (psychology); Speech recognition; Artificial intelligence; SIGNAL (programming language); Electroencephalography; Mathematics; Image (mathematics); Statistics; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000913146,0.0009311621,0.001174877,0.0005955331,0.0003467713,0.0008005309,0.0009952006,0.001420068,0.003934084],"category_scores_gemma":[0.003305935,0.0004871491,0.0008508206,0.001309115,0.0004811947,0.001199192,0.001244156,0.001055035,0.002064761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002054335,"about_ca_system_score_gemma":0.0009577254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007242202,"about_ca_topic_score_gemma":0.001955474,"domain_scores_codex":[0.9987513,0.0004773034,0.00006861423,0.0001870695,0.0004492422,0.00006653021],"domain_scores_gemma":[0.9987379,0.0004858378,0.0001279817,0.0001746948,0.0004226069,0.00005093022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005300631,0.0001302595,0.0005682753,0.000397523,0.0001942541,0.00008462526,0.0001719876,0.1335438,0.0781803,0.03443599,0.005982893,0.7457799],"study_design_scores_gemma":[0.00006726303,0.0002620777,0.0006324755,0.0000646216,0.00006476771,0.0003504706,0.00004679346,0.9401045,0.03048117,0.01512632,0.01273599,0.00006360275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001124695,0.000141904,0.9980234,0.0000440945,0.00002998118,0.000007090065,0.00002139975,0.000144108,0.0004633729],"genre_scores_gemma":[0.06032758,0.0005013707,0.9352063,0.0001875971,0.000089994,0.0001126809,0.0002400687,0.0001199846,0.003214462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003934084,"threshold_uncertainty_score":0.01316082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001995413171239,"score_gpt":0.2393095570293595,"score_spread":0.2292896028976471,"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."}}