{"id":"W2009915107","doi":"10.1118/1.2241851","title":"TH‐C‐330A‐09: Cascaded Systems Analysis of Noise Reduction Algorithms for Dual‐Energy Imaging","year":2006,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Noise reduction; Noise (video); Algorithm; Gaussian noise; Smoothing; Computer science; Optical transfer function; Reduction (mathematics); Artificial intelligence; Image noise; Image quality; Energy (signal processing); Computer vision; Mathematics; Optics; 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":[0.0001387876,0.0001559699,0.0003640259,0.000104923,0.00005804368,0.00001645352,0.0001071736,0.00006103754,0.00001066348],"category_scores_gemma":[0.00002333045,0.0001525716,0.0001752988,0.0006283342,0.0000746395,0.0001613735,0.00001894048,0.0001297976,0.000001908055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006235832,"about_ca_system_score_gemma":0.0000204839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002172332,"about_ca_topic_score_gemma":0.000005883,"domain_scores_codex":[0.9987896,0.00001629154,0.0003510818,0.000189178,0.0003794802,0.0002743409],"domain_scores_gemma":[0.9994917,0.0000724765,0.00007138919,0.0001988777,0.00007633682,0.00008921877],"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":[0.00001796758,0.0002006746,0.0004091306,0.0003266143,0.00086166,0.00002130574,0.0002681997,0.8094326,0.01695393,0.006183928,0.003521885,0.1618022],"study_design_scores_gemma":[0.0004266322,0.00001075311,0.0001764984,0.00006338685,0.0004805292,0.000008212813,0.00011781,0.9713274,0.02200168,0.002277769,0.002872184,0.0002371404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01462476,0.00104536,0.982278,0.00008106378,0.0007067326,0.00009593685,0.00003292937,0.0002000238,0.0009351262],"genre_scores_gemma":[0.9978634,0.00003948356,0.000712684,0.00001895591,0.001041164,0.00003873665,0.0001278793,0.00003577537,0.0001219338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9832386,"threshold_uncertainty_score":0.622169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007656428892855552,"score_gpt":0.2363633559112263,"score_spread":0.2287069270183708,"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."}}