{"id":"W4250167571","doi":"10.32920/ryerson.14648886","title":"Data Denoising in Analog and Digital Domains","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Noise reduction; Thresholding; Video denoising; Noise (video); Estimator; Computer science; Mean squared error; Artificial intelligence; Step detection; Pattern recognition (psychology); Algorithm; Mathematics; Statistics; Computer vision; Image (mathematics); Video processing","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":[0.002100588,0.0007051821,0.00100669,0.00130524,0.0004342501,0.00163886,0.001319042,0.001332641,0.001936872],"category_scores_gemma":[0.00575991,0.000422637,0.001217132,0.00105293,0.001442583,0.002374091,0.001312526,0.001914368,0.0009090148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005968263,"about_ca_system_score_gemma":0.0005430104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005120219,"about_ca_topic_score_gemma":0.0005767774,"domain_scores_codex":[0.9985929,0.000271345,0.0001179623,0.0003071737,0.0006612108,0.0000494426],"domain_scores_gemma":[0.9972435,0.001284159,0.0002322436,0.000575589,0.0006184061,0.0000460075],"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":[0.0001748203,0.0001075954,0.001563021,0.001062633,0.0001475581,0.0002333716,0.0004187664,0.124162,0.05716904,0.1875109,0.002888362,0.6245621],"study_design_scores_gemma":[0.00003469539,0.0002272037,0.001035719,0.0002571266,0.00009178949,0.0007800321,0.0001713523,0.7565463,0.09824103,0.09201955,0.05049519,0.00009999694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002423406,0.001028617,0.99472,0.0001762792,0.00009993904,0.00002326862,0.00002524848,0.0001073426,0.001395886],"genre_scores_gemma":[0.08194602,0.003883848,0.9069993,0.0004128607,0.000277366,0.0001183604,0.0001670255,0.0001418664,0.00605327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002100588,"threshold_uncertainty_score":0.01110911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06473722025479002,"score_gpt":0.3256396853513289,"score_spread":0.2609024650965389,"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."}}