{"id":"W2507910263","doi":"10.1109/icip.2016.7532703","title":"Notice of Removal Low-frequency image noise removal using white noise filter","year":2016,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"White noise; Notice; Noise (video); Computer science; Filter (signal processing); Acoustics; Computer vision; Image (mathematics); Artificial intelligence; Telecommunications; Physics; Political science","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":[0.001068102,0.0002529176,0.0003500814,0.0002089887,0.0001124909,0.0001404677,0.001148355,0.0001102953,0.0002619838],"category_scores_gemma":[0.0003304346,0.000170402,0.0001872651,0.0005213415,0.0001622594,0.001491509,0.0003798082,0.0001311725,0.0001583799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000719334,"about_ca_system_score_gemma":0.0001554502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009057588,"about_ca_topic_score_gemma":0.000003396106,"domain_scores_codex":[0.9975466,0.0003043581,0.0005359875,0.0005876776,0.0005147982,0.0005106293],"domain_scores_gemma":[0.9978153,0.0002761825,0.0002013268,0.001176804,0.0003734234,0.0001569797],"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":[0.00003136856,0.00006462877,0.0001262161,0.00003449218,0.00001653145,0.0004964247,0.0002392618,0.00001363769,0.9662058,0.003805635,0.0006525619,0.02831341],"study_design_scores_gemma":[0.004126899,0.0002860522,0.003095677,0.0006929409,0.00009031873,0.002043596,0.00003910146,0.04508753,0.9227157,0.01789504,0.002493317,0.001433859],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07760023,0.00007085472,0.8976206,0.0009093325,0.0005564836,0.000130861,0.000004878219,0.0001416591,0.02296514],"genre_scores_gemma":[0.07020306,0.000007992719,0.9224622,0.0005598175,0.0001712088,0.00000160368,5.624901e-7,0.0000251279,0.006568409],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04507389,"threshold_uncertainty_score":0.6948792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02586698416052395,"score_gpt":0.280013512921875,"score_spread":0.254146528761351,"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."}}