{"id":"W1894663092","doi":"10.1371/journal.pone.0137662","title":"Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Facilities Council; Natural Environment Research Council; Economic and Social Research Council; Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Hospital for Sick Children; Research Councils UK","keywords":"Wavelet; Autocovariance; Bayesian probability; Haar wavelet; Shrinkage estimator; Pattern recognition (psychology); Bayesian inference; Series (stratigraphy); Algorithm; Computer science; Artificial intelligence; Mathematics; Time series; Discrete wavelet transform; Wavelet transform; Statistics; Mean squared error; Mathematical analysis; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004330791,0.0004317389,0.0005522894,0.0008472009,0.000241464,0.0006653303,0.0006961295,0.0007745967,0.0008797093],"category_scores_gemma":[0.01241598,0.0003142599,0.0005346835,0.0006266509,0.0007340624,0.001197148,0.0007630643,0.00124756,0.0003609682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003356425,"about_ca_system_score_gemma":0.0005611377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001011095,"about_ca_topic_score_gemma":0.001061269,"domain_scores_codex":[0.9992249,0.0003459803,0.00003816227,0.0001153204,0.0002371162,0.00003846026],"domain_scores_gemma":[0.9976838,0.001537066,0.0002271554,0.0002326853,0.0002734103,0.00004592293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002438822,0.0000560611,0.002499305,0.0002027961,0.0001274013,0.000207193,0.0003153895,0.5657284,0.03166109,0.1709317,0.002102318,0.2259244],"study_design_scores_gemma":[0.000009605331,0.00002269205,0.001507277,0.00001903523,0.00001431684,0.00006785212,0.00001903088,0.9564789,0.002896185,0.03780259,0.001139268,0.00002328072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01558163,0.0001295161,0.9834463,0.00008566194,0.00001473438,0.000009755076,0.00003236498,0.00008342523,0.0006167095],"genre_scores_gemma":[0.508653,0.0009481357,0.4867066,0.0001001504,0.0001072055,0.000114699,0.0003255124,0.0002241963,0.002820402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004330791,"threshold_uncertainty_score":0.02290362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06495301464700097,"score_gpt":0.2519493106304688,"score_spread":0.1869962959834678,"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."}}