{"id":"W1844472376","doi":"10.1109/icassp.1982.1171579","title":"Estimation of coherence via ARMA modelling","year":2005,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Estimator; Autoregressive–moving-average model; Mathematics; Coherence (philosophical gambling strategy); Series (stratigraphy); Statistics; Autoregressive model; Spectral density estimation; Mean squared error; Moving average; Fourier series; Applied mathematics; Algorithm; Fourier transform; Mathematical analysis","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":[0.0001994109,0.00004395808,0.00007350839,0.00003969871,0.00002807527,0.00003135542,0.0002326714,0.00001872915,0.00002214381],"category_scores_gemma":[0.000006789779,0.00003797185,0.00002354609,0.0001350073,0.00001358834,0.0003980853,0.00002911456,0.00002750004,0.00004051639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008195771,"about_ca_system_score_gemma":0.00001386944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002015085,"about_ca_topic_score_gemma":4.45624e-7,"domain_scores_codex":[0.9995138,0.00003227839,0.0001305203,0.0001104542,0.0001259496,0.0000869827],"domain_scores_gemma":[0.9996498,0.00005443268,0.00003845395,0.0001912664,0.00004172263,0.00002429383],"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":[0.000001645577,0.00002043078,0.000003781637,0.000003348492,0.000001615011,7.470689e-7,0.0002066202,0.368155,0.003740836,0.00613249,0.00006814571,0.6216654],"study_design_scores_gemma":[0.00009661439,0.00001854587,0.00001413768,0.000004472288,0.000001233286,0.000004189201,0.000001187914,0.8877175,0.1057123,0.006229318,0.000157904,0.00004267538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005767359,0.00006306836,0.98871,0.0001853322,0.00005698191,0.00003552751,6.73511e-8,0.00005748223,0.005124144],"genre_scores_gemma":[0.4598067,0.000001498203,0.5398675,0.00006532213,0.00001537166,6.492285e-7,1.296973e-7,0.000001147448,0.000241682],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6216227,"threshold_uncertainty_score":0.1548447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03471848805807624,"score_gpt":0.2916029413058332,"score_spread":0.2568844532477569,"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."}}