{"id":"W4251382989","doi":"10.32920/ryerson.14665632","title":"Under-Determined Blind Source Separation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Initialization; Blind signal separation; Matching pursuit; Cluster analysis; Matching (statistics); Algorithm; Set (abstract data type); Computer science; Matrix (chemical analysis); Mixing (physics); Segmentation; Pattern recognition (psychology); Source separation; Mathematics; Mathematical optimization; Artificial intelligence; Statistics; Compressed sensing; Channel (broadcasting)","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":[0.001390341,0.001056399,0.001770645,0.0007371356,0.000567403,0.001951749,0.001141666,0.001702465,0.002341082],"category_scores_gemma":[0.005486134,0.0004988182,0.0006415949,0.0009914307,0.001583083,0.002832255,0.002664909,0.001583484,0.001112821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007860538,"about_ca_system_score_gemma":0.001529087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001492718,"about_ca_topic_score_gemma":0.0014678,"domain_scores_codex":[0.9966868,0.0008503728,0.0001463056,0.0009554509,0.001102891,0.0002583791],"domain_scores_gemma":[0.9967181,0.001218957,0.0003362342,0.0008611799,0.0007573376,0.0001081564],"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":[0.0008885303,0.0001648612,0.001791161,0.0008491703,0.0002749567,0.0005123016,0.000438585,0.3394036,0.06529795,0.1728514,0.01205724,0.4054702],"study_design_scores_gemma":[0.00004354844,0.0001419535,0.001303904,0.00005920339,0.00006728576,0.0005595936,0.0001118606,0.8701383,0.02800422,0.08075478,0.01870873,0.000106627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02050129,0.002166273,0.9723489,0.0003438211,0.0001793882,0.00003995625,0.0001674067,0.0002143314,0.004038603],"genre_scores_gemma":[0.571349,0.007016698,0.3974443,0.0006686281,0.001034272,0.0001537365,0.001031619,0.0001902436,0.02111152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002341082,"threshold_uncertainty_score":0.007831752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0444701577229451,"score_gpt":0.3316105414226108,"score_spread":0.2871403836996657,"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."}}