{"id":"W2090091618","doi":"10.1118/1.4889088","title":"SU‐F‐18C‐04: A Combination of Monoenergetic Reconstruction and Stoichiometric Calibration for Tissue Characterization Using Dual Energy Computed Tomography","year":2014,"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":"Hôpital Notre-Dame","funders":"","keywords":"Digital Enhanced Cordless Telecommunications; Imaging phantom; Calibration; Nuclear medicine; Iterative reconstruction; Radiation; Noise (video); Computer science; Physics; Optics; Medicine; Artificial intelligence; 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":[0.00009470172,0.00009621283,0.0001629245,0.0001482092,0.00005544625,0.00001601282,0.00003801558,0.00006701912,0.000003556199],"category_scores_gemma":[0.00004856701,0.0001036341,0.00002601542,0.000504058,0.00005491874,0.0002420982,0.00001219305,0.00006270978,1.105585e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000182837,"about_ca_system_score_gemma":0.00001042957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006331898,"about_ca_topic_score_gemma":0.000001138266,"domain_scores_codex":[0.9993566,0.00002541343,0.0002112594,0.0001167298,0.0001731284,0.0001168352],"domain_scores_gemma":[0.9996527,0.00008369373,0.0000702422,0.00007197851,0.0000573793,0.0000639925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001172926,0.00007090315,0.0006577564,0.000204052,0.00003317057,3.253485e-7,0.0001227448,0.007758836,0.09652579,0.006026344,0.00001022132,0.8885781],"study_design_scores_gemma":[0.0004685465,0.00005960237,0.0009451726,0.00005629571,0.00002122014,0.000004714466,0.00000940568,0.9196618,0.0737253,0.004779741,0.0001524877,0.0001157617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3247305,0.00003624882,0.6748242,0.00001800591,0.0002562774,0.00005630045,0.000004981745,0.0000563921,0.00001705319],"genre_scores_gemma":[0.9971,0.00002235226,0.00243697,0.00002856793,0.0002329343,0.000008882247,0.0001478077,0.00001958482,0.000002911279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9119029,"threshold_uncertainty_score":0.4226077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00756768926295211,"score_gpt":0.2142235495534258,"score_spread":0.2066558602904736,"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."}}