{"id":"W1651493696","doi":"10.1109/adfsp.1998.685719","title":"Wavelet de-noising of coarsely quantized signals","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Wavelet; Thresholding; Gaussian noise; Noise (video); SIGNAL (programming language); Computer science; Quantization (signal processing); Wavelet transform; Artificial intelligence; Gaussian; Algorithm; Noise measurement; Pattern recognition (psychology); Step detection; Mathematics; Noise reduction; Computer vision; Physics; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0007779076,0.00009781079,0.0002037605,0.0001030484,0.0000637127,0.00009355709,0.00055504,0.00004927211,0.0002754498],"category_scores_gemma":[0.0001120815,0.00008358187,0.0000861512,0.0003492968,0.00004297615,0.0003061517,0.00009746,0.00008298639,0.00007030953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001499651,"about_ca_system_score_gemma":0.00002122395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004524999,"about_ca_topic_score_gemma":8.935652e-7,"domain_scores_codex":[0.9988297,0.0002047102,0.0002560395,0.0002135547,0.0002319569,0.0002640464],"domain_scores_gemma":[0.9991138,0.0002721883,0.000081782,0.0003729451,0.00008969344,0.00006956363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001931808,0.0002212036,0.0001781148,0.00004468124,0.0000444284,0.0001101837,0.002199967,0.000150576,0.5554885,0.06109629,0.01513672,0.36531],"study_design_scores_gemma":[0.001181389,0.0001568466,0.0004260374,0.00004826978,0.00001278839,0.00007216052,0.00003032644,0.4638623,0.5185717,0.01211533,0.003213751,0.0003090346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01764714,0.000197366,0.9577193,0.0004520589,0.0001073303,0.00005791347,4.169482e-7,0.0001086566,0.02370982],"genre_scores_gemma":[0.4978299,0.00001292241,0.4994078,0.0005760303,0.00002410677,0.00000110554,1.43999e-7,0.000005673507,0.002142305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4801827,"threshold_uncertainty_score":0.340837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05915768945264739,"score_gpt":0.2895681171414439,"score_spread":0.2304104276887965,"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."}}