{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000815249,0.0008147881,0.0003644676,0.0004965016,0.0003836371,0.0005508195,0.0007408801,0.0007675319,0.003415652],"category_scores_gemma":[0.001877375,0.000270489,0.0007460933,0.0003388513,0.0003090694,0.0005541639,0.0004697812,0.0007599117,0.0007591184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007407537,"about_ca_system_score_gemma":0.001127478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005277788,"about_ca_topic_score_gemma":0.008030619,"domain_scores_codex":[0.9997072,0.00007236264,0.00001254513,0.00004344781,0.0001460851,0.0000183371],"domain_scores_gemma":[0.9992495,0.0003259825,0.00005973719,0.000079221,0.0002576313,0.00002793651],"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":[0.0002343005,0.0001674611,0.001572873,0.0001799187,0.0001337289,0.000154876,0.0001500808,0.7436712,0.04721555,0.01828854,0.00548264,0.1827487],"study_design_scores_gemma":[0.000002630927,0.00001662436,0.0001733838,0.000002535399,0.00000288055,0.0000105904,0.000001979428,0.9967505,0.001931546,0.0004710653,0.0006315479,0.000004665719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02158853,0.0001135623,0.9756949,0.00008825796,0.00004250625,0.00007018061,0.00008020458,0.0006505708,0.001671204],"genre_scores_gemma":[0.3329997,0.0002637853,0.6596736,0.00008357636,0.0000636098,0.0003280777,0.0004389215,0.0003468527,0.005801917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005277788,"threshold_uncertainty_score":0.01142645,"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."}}