{"id":"W1606069290","doi":"10.1007/978-3-540-92957-4_26","title":"A Self-governing Hybrid Model for Noise Removal","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Solver; Smoothing; Noise reduction; Convergence (economics); Nonlinear system; Filter (signal processing); Noise (video); Computer science; Algorithm; Stability (learning theory); Mathematical optimization; Rate of convergence; Image (mathematics); Image denoising; Mathematics; Control theory (sociology); Applied mathematics; Artificial intelligence; Computer vision; Machine learning","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":[0.0003658782,0.0006145244,0.0008910304,0.0003695442,0.0003991239,0.00118564,0.001755637,0.002195846,0.002961412],"category_scores_gemma":[0.0008851017,0.0005476421,0.0008700192,0.0004253344,0.0009682651,0.001284731,0.001276665,0.001240419,0.0009386574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004549627,"about_ca_system_score_gemma":0.0004920968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002750931,"about_ca_topic_score_gemma":0.002565959,"domain_scores_codex":[0.9998304,0.00003771109,0.000008747114,0.00005028996,0.00005385738,0.00001904134],"domain_scores_gemma":[0.999741,0.00009620962,0.00004058551,0.00002905907,0.00007133591,0.00002169575],"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":[0.00004881793,0.00006115035,0.0002882672,0.00009957623,0.00006116878,0.0001174,0.00009742152,0.8924996,0.008683268,0.08337639,0.001615507,0.01305151],"study_design_scores_gemma":[0.000003128642,0.000008688807,0.00003344201,0.000002673334,0.00000619708,0.00001504684,0.000004051003,0.9948007,0.0002407081,0.004436069,0.0004426626,0.00000665536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01405901,0.0004507754,0.9755884,0.0002726548,0.0001677861,0.00002733769,0.00007690471,0.0002006707,0.009156478],"genre_scores_gemma":[0.8106751,0.001332598,0.1056207,0.0005421101,0.0002368955,0.000257909,0.0003401416,0.0003208429,0.08067366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002961412,"threshold_uncertainty_score":0.009906948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02450594759376404,"score_gpt":0.2700781098056551,"score_spread":0.2455721622118911,"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."}}