{"id":"W3029441321","doi":"10.1002/mp.14309","title":"Electron density and effective atomic number estimation in a maximum a <i>posteriori</i> framework for dual‐energy computed tomography","year":2020,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Effective atomic number; Atomic number; Digital Enhanced Cordless Telecommunications; Estimator; Imaging phantom; Maximum a posteriori estimation; Algorithm; Calibration; Physics; Mathematics; Computational physics; Computer science; Statistics; Optics; Atomic physics; Maximum likelihood","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002587808,0.0009278459,0.0006660983,0.0008322527,0.0003050893,0.001162034,0.001493092,0.001108419,0.0008674233],"category_scores_gemma":[0.004714179,0.0007998595,0.0009208763,0.0004363084,0.001133514,0.0009911333,0.001449788,0.001030588,0.0003259143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008681423,"about_ca_system_score_gemma":0.001232178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003336568,"about_ca_topic_score_gemma":0.002672987,"domain_scores_codex":[0.9993326,0.0003160529,0.00002398947,0.0001012198,0.000194794,0.00003141529],"domain_scores_gemma":[0.9985311,0.0009333923,0.0001913153,0.00009942681,0.0001953245,0.00004946991],"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":[0.0001001993,0.00003329956,0.0007664657,0.0001723467,0.00006008229,0.00008852049,0.00008794704,0.9311215,0.0105914,0.01652127,0.0005580027,0.03989897],"study_design_scores_gemma":[0.000004325402,0.00001222987,0.0001641739,0.000008780662,0.000008015658,0.00003157599,0.000005403685,0.9935436,0.001334172,0.004380694,0.0004984788,0.000008532948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003415998,0.0001254379,0.9959852,0.00006070141,0.000004548656,0.00001180326,0.00001866235,0.00008780722,0.0002899495],"genre_scores_gemma":[0.2299402,0.0005859244,0.7661471,0.0001734441,0.00006221856,0.0002700292,0.0003068276,0.0003015168,0.00221275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003336568,"threshold_uncertainty_score":0.01368582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004271216446202473,"score_gpt":0.228795468256282,"score_spread":0.2245242518100795,"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."}}