{"id":"W3201339010","doi":"10.1101/2021.05.10.443467","title":"Subspace based Multiple Constrained Minimum Variance (SMCMV) Beamformers","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Subspace topology; Computer science; Noise (video); Algorithm; Ideal (ethics); Covariance; Set (abstract data type); Variance (accounting); Identification (biology); Artificial intelligence; Minimum-variance unbiased estimator; Simplicity; SIGNAL (programming language); Pattern recognition (psychology); Speech recognition; Mathematics; Mean squared error; Image (mathematics); Statistics","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":[0.001151238,0.0008368296,0.0009132565,0.0005547735,0.0002337577,0.0005923649,0.000864586,0.001090388,0.003078286],"category_scores_gemma":[0.003599662,0.0004557723,0.0007061027,0.0009976332,0.000562582,0.0008004679,0.0009168076,0.000731708,0.001272417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002428422,"about_ca_system_score_gemma":0.0006801696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009785253,"about_ca_topic_score_gemma":0.001851094,"domain_scores_codex":[0.9992265,0.0003984013,0.0000347705,0.0001103219,0.000196005,0.00003412599],"domain_scores_gemma":[0.9989773,0.0005531783,0.0001031862,0.0001378728,0.0001941631,0.00003440816],"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":[0.0002550916,0.00005626963,0.0009734324,0.0002637861,0.0002496025,0.0001186337,0.0001867388,0.434723,0.04432475,0.05347181,0.005474585,0.4599023],"study_design_scores_gemma":[0.00002480641,0.00008140747,0.0004018911,0.00002187785,0.00002202476,0.000184882,0.0000177801,0.9692054,0.009389148,0.01478028,0.005841405,0.00002914884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001992089,0.0001389565,0.9972364,0.00003856097,0.00001394678,0.000009873277,0.00002990391,0.0002134538,0.0003267823],"genre_scores_gemma":[0.07776887,0.0003034629,0.919414,0.0001094045,0.00003717171,0.0001181238,0.0002460334,0.000112652,0.001890152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003078286,"threshold_uncertainty_score":0.01029789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297457751738542,"score_gpt":0.2112553680903473,"score_spread":0.1982807905729618,"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."}}