{"id":"W2137718389","doi":"10.1109/iscas.2007.378461","title":"An Identification Technique for Noisy ARMA Systems in Correlation Domain","year":2007,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Autocorrelation; Noise (video); Autoregressive model; Residual; SIGNAL (programming language); Algorithm; Autoregressive–moving-average model; Noise measurement; Frequency domain; Computer science; Mathematics; Spectral density; Signal-to-noise ratio (imaging); Speech recognition; Statistics; Noise reduction; Artificial intelligence","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":[0.002959596,0.00007754107,0.0000880599,0.0003047875,0.00005475242,0.000167214,0.0003903643,0.0001080525,0.000001622594],"category_scores_gemma":[0.00002351529,0.00007759403,0.00002491759,0.0003921675,0.00001350751,0.000867496,0.00002336646,0.00008068071,0.000009455766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000798671,"about_ca_system_score_gemma":0.0000289714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007091332,"about_ca_topic_score_gemma":0.00007936782,"domain_scores_codex":[0.9989636,0.00007416899,0.0003797789,0.0002657924,0.0001580915,0.0001585818],"domain_scores_gemma":[0.9992186,0.00009191999,0.0001222002,0.0004129975,0.0001095599,0.00004469329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009239355,0.00009251092,0.000675671,0.00001300088,0.000001692162,0.000001286865,0.0008864371,0.0005204942,0.1201319,0.8728785,0.0004235884,0.004365675],"study_design_scores_gemma":[0.0007085683,0.0003144469,0.0181684,0.00005653915,0.000003732771,0.00003554398,0.0004984941,0.4411399,0.4497898,0.08122254,0.00749618,0.0005658958],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007323361,0.00001338589,0.9890226,0.000186654,0.0001493126,0.001146708,8.200841e-7,0.0004632139,0.001693944],"genre_scores_gemma":[0.7818654,0.000001055247,0.2176427,0.0001113234,0.00002562528,0.0001930056,0.00001157681,0.00000648119,0.0001428782],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.791656,"threshold_uncertainty_score":0.3164193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01562330866214065,"score_gpt":0.3065361203779944,"score_spread":0.2909128117158537,"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."}}