{"id":"W2142142556","doi":"10.1109/icassp.2008.4518333","title":"Maximum likelihood binary detection in improper complex gaussian noise","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Detector; Matched filter; Binary number; Gaussian noise; Filter (signal processing); Algorithm; Measure (data warehouse); Computer science; Gaussian; Noise (video); Complex normal distribution; SIGNAL (programming language); Detection theory; Signal-to-noise ratio (imaging); Mathematics; Artificial intelligence; Physics; Telecommunications; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003752301,0.0002123289,0.0002185338,0.0003066029,0.0002149964,0.0002453786,0.0009443806,0.0001135697,0.00002376],"category_scores_gemma":[0.00005606656,0.0001673211,0.00005910265,0.0003081846,0.0002020099,0.0008210191,0.0001957692,0.0003918367,0.000005102223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008260955,"about_ca_system_score_gemma":0.0001387226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003902553,"about_ca_topic_score_gemma":0.000005351213,"domain_scores_codex":[0.9983158,0.00001686549,0.0004288677,0.0004180865,0.0005794523,0.0002409834],"domain_scores_gemma":[0.9987391,0.00002858745,0.0003432235,0.0001066876,0.0007078317,0.00007462309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001075075,0.000251879,0.001424496,0.0001055359,0.00001916666,0.000008122891,0.001968536,0.00009025256,0.9216297,0.005671084,0.0002852689,0.0684385],"study_design_scores_gemma":[0.0008911179,0.0004039284,0.01448081,0.0006007243,0.00001673022,0.0001968947,0.0005864693,0.6598255,0.2779919,0.044301,0.0001635513,0.0005413581],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7797897,0.00004181032,0.194045,0.003102076,0.0003626329,0.0004988696,0.000008830486,0.0002604873,0.02189058],"genre_scores_gemma":[0.984925,0.00006945906,0.01429744,0.0004254654,0.00009314856,0.00001565016,0.000001047658,0.00001506001,0.0001577486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6597353,"threshold_uncertainty_score":0.6823155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04638481127931506,"score_gpt":0.2801136698612019,"score_spread":0.2337288585818868,"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."}}