{"id":"W2132288502","doi":"10.1109/icassp.1996.547968","title":"Detection for binary transmission based on the empirical characteristic function","year":2002,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Detector; Binary number; Algorithm; Gaussian noise; Noise (video); Gaussian; Computer science; Transmission (telecommunications); Detection theory; Inference; Function (biology); Additive white Gaussian noise; Mathematics; Statistics; Artificial intelligence; White noise; Physics; Telecommunications; Image (mathematics)","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":[0.001380343,0.0004488647,0.000957428,0.0008073351,0.000300908,0.0008139768,0.0008294948,0.0006901135,0.001115904],"category_scores_gemma":[0.007105845,0.0002751881,0.0003042949,0.0006916365,0.0009078701,0.001503056,0.0009376652,0.001054585,0.0004737222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006582497,"about_ca_system_score_gemma":0.0006804402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005844048,"about_ca_topic_score_gemma":0.0007041197,"domain_scores_codex":[0.9990327,0.0002836247,0.00004445296,0.0001634311,0.0003790114,0.00009683685],"domain_scores_gemma":[0.9972312,0.001482578,0.0004441813,0.0003806319,0.0003853794,0.0000760803],"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":[0.0006492458,0.0001012314,0.003052662,0.0002192642,0.0001029156,0.0001748648,0.0001957598,0.1753464,0.08209,0.1011194,0.003374228,0.6335741],"study_design_scores_gemma":[0.00002024862,0.00007549321,0.0005955531,0.00001572463,0.00001718544,0.0002932042,0.00001022816,0.9584793,0.02539152,0.01329188,0.001771115,0.00003857511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01429761,0.0002525408,0.9846098,0.00007979111,0.00002130077,0.0000109176,0.00002033555,0.000221939,0.0004858258],"genre_scores_gemma":[0.3349384,0.000329928,0.6621944,0.0001050088,0.00007253123,0.00005577362,0.00008397272,0.00009889104,0.002121097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001380343,"threshold_uncertainty_score":0.007300019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1016992884881107,"score_gpt":0.2873055205529458,"score_spread":0.1856062320648352,"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."}}