{"id":"W2124989052","doi":"10.1007/s11265-007-0156-4","title":"On Bandwidth Selection in Local Polynomial Regression Analysis and Its Application to Multi-resolution Analysis of Non-uniform Data","year":2008,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Saskatchewan; University of Missouri","keywords":"Bandwidth (computing); Algorithm; Computer science; Polynomial regression; Polynomial; Regression analysis; Signal processing; Mathematics; Digital signal processing; Machine learning; Telecommunications","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":[0.003876925,0.0007985047,0.00112969,0.001429458,0.0005402793,0.001035201,0.001046746,0.001510959,0.001386216],"category_scores_gemma":[0.0133333,0.000466838,0.0008836241,0.00165545,0.00130693,0.001475928,0.001291747,0.00154407,0.0005725559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003788227,"about_ca_system_score_gemma":0.00048188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001909813,"about_ca_topic_score_gemma":0.002147589,"domain_scores_codex":[0.9987057,0.0006959398,0.00006550107,0.0001658386,0.0002932692,0.00007371666],"domain_scores_gemma":[0.9941899,0.004559568,0.0002047409,0.000401121,0.000554363,0.00009027974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004702284,0.0001875705,0.001473102,0.0005957671,0.0001770684,0.0005412283,0.000406456,0.2517387,0.06870777,0.1040807,0.002935947,0.5686855],"study_design_scores_gemma":[0.0000113114,0.0000423097,0.00048837,0.00002973888,0.00003996541,0.0001250569,0.00002752751,0.9678049,0.008166312,0.02124091,0.001993884,0.00002969702],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004626862,0.0007259677,0.9940469,0.0001126817,0.00002971287,0.00001106263,0.000008088555,0.00009156959,0.0003471407],"genre_scores_gemma":[0.2612644,0.005664386,0.7265795,0.0002246751,0.0004303903,0.000125642,0.0001307722,0.0005131665,0.005067077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003876925,"threshold_uncertainty_score":0.0205034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03720577878590518,"score_gpt":0.3207950727128988,"score_spread":0.2835892939269936,"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."}}