{"id":"W2896080268","doi":"10.1088/1361-6560/ab51db","title":"Block matching frame based material reconstruction for spectral CT","year":2019,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Correctness; Regularization (linguistics); Iterative reconstruction; Imaging phantom; Block (permutation group theory); Matching (statistics); Tomography; Total variation denoising","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":[0.00006482925,0.00007595617,0.0001604352,0.00003010628,0.00001572855,0.000002806942,0.00003229742,0.00002477651,0.00002248044],"category_scores_gemma":[0.00000610385,0.00006262978,0.00001358715,0.00004241276,0.00004085694,0.00004391712,0.000005548335,0.00009381433,0.000002343984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001588968,"about_ca_system_score_gemma":0.00000550302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001173637,"about_ca_topic_score_gemma":0.000002279987,"domain_scores_codex":[0.9996257,0.000008329369,0.0001067056,0.0001032049,0.00001630431,0.0001397556],"domain_scores_gemma":[0.9998387,0.0000613695,0.00001693109,0.00005950524,0.000006876799,0.0000165925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001001707,0.00002417741,0.009988083,0.0003824975,0.00004102763,0.000002996071,0.0006893778,0.01664376,0.8375032,0.01264817,0.00009917153,0.1218774],"study_design_scores_gemma":[0.008760802,0.000981683,0.002797193,0.0008062027,0.00008529054,0.0001267574,0.002247316,0.238107,0.1536295,0.5814841,0.009825286,0.001148879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9767743,0.00007527647,0.02132834,0.0001357804,0.0009066515,0.0001145707,0.000006390118,0.00004538757,0.0006132895],"genre_scores_gemma":[0.9965818,0.00002760435,0.002682314,0.0001238292,0.0005401749,0.000008188481,0.00002002137,0.000009306,0.000006796191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6838737,"threshold_uncertainty_score":0.2553968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04714226632703355,"score_gpt":0.3108074869524469,"score_spread":0.2636652206254133,"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."}}