{"id":"W4307743471","doi":"10.32920/21428694","title":"Noiseless Codelength in Wavelet Denoising","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Thresholding; Orthonormal basis; Subspace topology; Linear subspace; Wavelet; Pattern recognition (psychology); Noise reduction; Noise (video); Algorithm; Mathematics; Artificial intelligence; Computer science; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001758834,0.0003212174,0.0004884826,0.0004443244,0.0001412682,0.0004767795,0.002625559,0.0001892992,0.0002474329],"category_scores_gemma":[0.00008055175,0.0003262658,0.0001569533,0.0005312508,0.00003184413,0.0002578011,0.005144768,0.001387334,0.00002912684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000252591,"about_ca_system_score_gemma":0.0003610067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004619597,"about_ca_topic_score_gemma":0.0000441967,"domain_scores_codex":[0.9968308,0.0006242341,0.0004975718,0.0009836312,0.00057284,0.0004909708],"domain_scores_gemma":[0.9981062,0.0002528929,0.0001591054,0.001315034,0.00007414712,0.00009261049],"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":[0.0000888598,0.0007754023,0.001242638,0.0006701997,0.0001452934,0.003045885,0.00886343,0.04273572,0.002801808,0.193877,0.01067198,0.7350817],"study_design_scores_gemma":[0.002640982,0.0001749329,0.00411547,0.0003963571,0.00004026825,0.0001288703,0.0002778668,0.7771301,0.008919816,0.1761922,0.02685978,0.003123431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0128496,0.0003975031,0.9529279,0.0006797023,0.001362901,0.0002829434,0.000005297816,0.000291186,0.0312029],"genre_scores_gemma":[0.278421,0.0001103156,0.7144609,0.00180761,0.0001947952,0.0001004725,0.00002905508,0.00005486325,0.004821001],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7343943,"threshold_uncertainty_score":0.9999189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03684604045162796,"score_gpt":0.3082249476240315,"score_spread":0.2713789071724035,"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."}}