{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009117037,0.0005660221,0.0003041415,0.0007210504,0.000137119,0.0007958384,0.0007915244,0.0006672439,0.0008364568],"category_scores_gemma":[0.001167279,0.0004667072,0.0001821183,0.0004642385,0.0003087405,0.0005273502,0.0003718251,0.0003461335,0.0002824091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003522341,"about_ca_system_score_gemma":0.0004248834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001019554,"about_ca_topic_score_gemma":0.001782153,"domain_scores_codex":[0.9997208,0.00004258203,0.00001434797,0.0000638155,0.0001417858,0.00001670822],"domain_scores_gemma":[0.9996669,0.00007180718,0.00008392595,0.00007334197,0.00008077724,0.00002317839],"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":[0.0007121073,0.0001214075,0.006280805,0.0001788551,0.00007137196,0.0002355889,0.00005942097,0.01182078,0.8703843,0.0007713828,0.0006547445,0.1087093],"study_design_scores_gemma":[0.00005650156,0.0004523214,0.008938503,0.00002285117,0.00007858047,0.001795274,0.0000244094,0.2291316,0.7532059,0.0003014345,0.005926013,0.00006666901],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4810835,0.002292038,0.511193,0.0001638751,0.00006072685,0.0001374372,0.0003486794,0.00201679,0.002703959],"genre_scores_gemma":[0.4716709,0.000614857,0.5250655,0.00005053079,0.00001568838,0.00008670249,0.0004693795,0.0002433916,0.001783081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001019554,"threshold_uncertainty_score":0.004821599,"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."}}