{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006063716,0.0003328592,0.0004667123,0.0004552075,0.0001402595,0.0002128092,0.002003015,0.000509071,0.00001419851],"category_scores_gemma":[0.00006061172,0.0003706136,0.0001635918,0.0006446601,0.00009087978,0.0002513712,0.002053842,0.001217955,0.0001171502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000161344,"about_ca_system_score_gemma":0.0002482717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001683074,"about_ca_topic_score_gemma":0.0001166387,"domain_scores_codex":[0.9975952,0.0003999399,0.0002649311,0.001160078,0.0001223425,0.0004575384],"domain_scores_gemma":[0.997592,0.0002212872,0.0002032455,0.001645178,0.0001599198,0.0001783982],"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":[0.00007613181,0.0003760952,0.001177897,0.0002244456,0.0001399648,0.01325067,0.0006271895,0.5231282,0.004615705,0.4515321,0.00079668,0.004054908],"study_design_scores_gemma":[0.001158139,0.00006724568,0.001552479,0.0002355248,0.0000611408,0.00007450762,0.000030885,0.9416229,0.0009659983,0.0517397,0.001527896,0.0009636329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3970768,0.0001236305,0.5990689,0.00005536547,0.002144464,0.0002508241,0.000009670877,0.0001679231,0.001102469],"genre_scores_gemma":[0.983629,0.00008509456,0.01407903,0.00009255835,0.0001111747,0.000001257435,0.00001004827,0.00002129829,0.001970565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5865522,"threshold_uncertainty_score":0.9998746,"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."}}