{"id":"W2512008777","doi":"10.1118/1.4961757","title":"Sci‐Thur AM: YIS – 07: Optimizing dual‐energy x‐ray parameters using a single filter for both high and low‐energy images to enhance soft‐tissue imaging","year":2016,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Health Authority; Dalhousie University","funders":"","keywords":"Imaging phantom; Materials science; Filter (signal processing); Biomedical engineering; Dual energy; Energy (signal processing); Soft tissue; Nuclear medicine; Physics; Computer science; Medicine; Radiology; Computer vision; Bone mineral","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":[0.0004767619,0.0006870823,0.0002470192,0.000473044,0.0001612329,0.0005389572,0.0004686016,0.0006348124,0.01324629],"category_scores_gemma":[0.0004576589,0.0002583476,0.0003098619,0.0003080304,0.0002401356,0.0004598687,0.0004866286,0.0002733498,0.003183699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000490594,"about_ca_system_score_gemma":0.0006590617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008768289,"about_ca_topic_score_gemma":0.001299886,"domain_scores_codex":[0.9998592,0.00001582621,0.000004994582,0.00001894234,0.00007994944,0.00002097714],"domain_scores_gemma":[0.9998397,0.00002018043,0.00002806785,0.00001804152,0.00007209063,0.0000219569],"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":[0.002062419,0.0005242841,0.003912278,0.000726998,0.0001363598,0.0003082234,0.0001216704,0.06835099,0.7637983,0.003677814,0.01947279,0.1369078],"study_design_scores_gemma":[0.0002752407,0.001408342,0.009111023,0.00004602292,0.0001251277,0.000782155,0.00007773714,0.3500115,0.5972486,0.0006309834,0.04020763,0.00007569494],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6779165,0.001620346,0.2375719,0.0006992773,0.0001822345,0.0003999217,0.002308737,0.01134511,0.06795601],"genre_scores_gemma":[0.7642546,0.0006115642,0.196399,0.0002838201,0.00006250144,0.0002617251,0.002775882,0.00221347,0.0331374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01324629,"threshold_uncertainty_score":0.04431325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008922464020343,"score_gpt":0.2441890819013511,"score_spread":0.2352666178810081,"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."}}