{"id":"W1972176923","doi":"10.48550/arxiv.1002.2421","title":"Nonhomogeneous Wavelet Systems in High Dimensions","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Wavelet; Discrete wavelet transform; Orthonormal basis; Wavelet transform; Stationary wavelet transform; Cascade algorithm; Mathematics; Wavelet packet decomposition; Second-generation wavelet transform; Lifting scheme; Mathematical analysis; Algorithm; Computer science; Artificial intelligence; 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":[0.0008808039,0.0007105911,0.0008115152,0.001274268,0.0008292667,0.001521727,0.0006611068,0.0007982909,0.004347543],"category_scores_gemma":[0.002039681,0.0004029033,0.000439177,0.0004823409,0.00142495,0.002096502,0.001798713,0.001275162,0.0005327843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006106409,"about_ca_system_score_gemma":0.0003658838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003200739,"about_ca_topic_score_gemma":0.0003119724,"domain_scores_codex":[0.9993556,0.0001700809,0.0000434612,0.0001558553,0.000156651,0.0001183466],"domain_scores_gemma":[0.9990886,0.0002155826,0.0002666345,0.000133049,0.0001348128,0.0001612185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006005708,0.00004893001,0.0008793013,0.00009096993,0.00002325972,0.0002580518,0.0002551383,0.01315379,0.0135548,0.9607609,0.0008748297,0.01004002],"study_design_scores_gemma":[0.00004482498,0.000268867,0.002133065,0.00004706546,0.00002374653,0.0003174185,0.0003949048,0.2366058,0.005275105,0.7465629,0.008282948,0.00004329956],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5003269,0.001465242,0.4779759,0.000563011,0.000201294,0.00009547763,0.0002044384,0.0001638899,0.01900379],"genre_scores_gemma":[0.9091897,0.0007485094,0.07819883,0.0001221533,0.0002642767,0.0001319829,0.0002888271,0.00006769216,0.01098802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004347543,"threshold_uncertainty_score":0.01454395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0548228774871431,"score_gpt":0.1989525186551343,"score_spread":0.1441296411679912,"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."}}