{"id":"W2116568723","doi":"10.1109/scft.2000.878414","title":"Partial-energy weighted interpolation of linear prediction coefficients","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Interpolation (computer graphics); Weighting; Linear interpolation; Mathematics; Energy (signal processing); Frame (networking); Line (geometry); Representation (politics); Linear prediction; Algorithm; Computer science; Mathematical analysis; Statistics; Telecommunications; Geometry; Acoustics; Physics","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":[0.001022826,0.0006762368,0.0006019949,0.0005511884,0.0002337145,0.0005339374,0.0006011552,0.000453518,0.002839428],"category_scores_gemma":[0.003445878,0.0002667496,0.000536971,0.0007802968,0.0003117961,0.001135811,0.0005397723,0.0008145226,0.001147851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002286174,"about_ca_system_score_gemma":0.0003408514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001087944,"about_ca_topic_score_gemma":0.001483915,"domain_scores_codex":[0.9995547,0.0001125238,0.0000218891,0.00007784346,0.0001932538,0.00003975615],"domain_scores_gemma":[0.9990292,0.0004479686,0.00006536577,0.0001857002,0.0002491559,0.0000226703],"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":[0.000693736,0.00008131181,0.001233698,0.0002157551,0.00007438961,0.000168494,0.0001502213,0.1275013,0.09189007,0.01545135,0.001754342,0.7607852],"study_design_scores_gemma":[0.00001286251,0.0001602454,0.001131152,0.00002861005,0.00004356061,0.0002431715,0.00002743432,0.9133854,0.07221031,0.00476762,0.007949005,0.00004065309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01933132,0.0002578183,0.97878,0.00003626694,0.00005998279,0.00001715729,0.00004166432,0.000451474,0.001024308],"genre_scores_gemma":[0.2033568,0.0006534631,0.789718,0.00004744147,0.00007506247,0.00005320113,0.0003917075,0.0002897085,0.005414501],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002839428,"threshold_uncertainty_score":0.009498775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0246309176763775,"score_gpt":0.2568255699152326,"score_spread":0.2321946522388551,"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."}}