{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005108182,0.0001294741,0.0002052606,0.00001462985,0.00003457991,0.00001448663,0.00005222557,0.0000833028,0.000002807751],"category_scores_gemma":[0.00006306446,0.0001358357,0.00004202797,0.0002459846,0.000042006,0.0001007481,0.00002979242,0.0002551456,0.000002985577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003112572,"about_ca_system_score_gemma":0.00001273079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004558257,"about_ca_topic_score_gemma":0.00000307697,"domain_scores_codex":[0.9993243,0.00001855751,0.0001299859,0.0001684839,0.0001450103,0.0002136274],"domain_scores_gemma":[0.9995642,0.0002052658,0.00002095334,0.00006746858,0.00001680775,0.000125283],"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":[0.0005342297,0.0003026151,0.01311909,0.001470785,0.000335865,0.0001157044,0.004431996,0.02137992,0.00608537,0.02727449,0.001138615,0.9238113],"study_design_scores_gemma":[0.001583376,0.000131567,0.003001334,0.0002756562,0.00004007651,0.00001307652,0.00002395912,0.7191035,0.03827997,0.2365244,0.0005856837,0.0004373675],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2546898,0.00008045544,0.7446523,0.0002055707,0.00008129837,0.0001308489,0.000002438736,0.0001212545,0.00003599915],"genre_scores_gemma":[0.992549,0.00001400597,0.006362238,0.0008083435,0.0001868165,0.00003860115,0.00001654632,0.00002409312,3.276139e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9233739,"threshold_uncertainty_score":0.5539221,"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."}}