{"id":"W2097907657","doi":"10.1155/asp.2005.649","title":"A MUSIC-Based Algorithm for Blind User Identification in Multiuser DS-CDMA","year":2005,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Code division multiple access; Subspace topology; Computer science; Linear subspace; SIGNAL (programming language); Ambiguity; Algorithm; Multiuser detection; Signal subspace; Identification (biology); Interference (communication); Code (set theory); Scheme (mathematics); Transformation (genetics); Process (computing); Speech recognition; Theoretical computer science; Mathematics; Artificial intelligence; Telecommunications","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":[0.0007977077,0.0006945943,0.0006690297,0.0008517664,0.0006959368,0.0006959679,0.0007229949,0.0007916541,0.001553606],"category_scores_gemma":[0.002064549,0.0002878656,0.00045601,0.0007891362,0.0006557817,0.0009992631,0.00082539,0.0008693616,0.001207428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004406609,"about_ca_system_score_gemma":0.0009360188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001022998,"about_ca_topic_score_gemma":0.001324037,"domain_scores_codex":[0.9992595,0.0002213654,0.00005097996,0.000100084,0.0003221913,0.00004575597],"domain_scores_gemma":[0.9995283,0.0001617935,0.00004538033,0.0000679599,0.0001724505,0.00002421406],"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":[0.0004148403,0.00007973962,0.0005451501,0.0001326372,0.00005353077,0.00009733828,0.0001322815,0.09258246,0.03533052,0.04368217,0.002906016,0.8240433],"study_design_scores_gemma":[0.00004805263,0.0001231227,0.0003593226,0.00001524331,0.00001500711,0.0002066648,0.00001968654,0.9612347,0.01850793,0.01390818,0.00552138,0.00004071284],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002278649,0.0001513341,0.9967716,0.00003884347,0.00002859755,0.00002497603,0.00001425525,0.000276569,0.0004151027],"genre_scores_gemma":[0.05787167,0.0002080304,0.9399793,0.00005370129,0.00004479818,0.0001245187,0.00006572797,0.00003989874,0.001612372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001553606,"threshold_uncertainty_score":0.005197287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02861682257950453,"score_gpt":0.3375854192288836,"score_spread":0.308968596649379,"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."}}