{"id":"W2025716635","doi":"10.1118/1.1901203","title":"Generalized DQE analysis of radiographic and dual‐energy imaging using flat‐panel detectors","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Detective quantum efficiency; Flat panel detector; Radiography; Optics; Detector; Medical imaging; Computed radiography; Physics; X-ray detector; Digital radiography; Nuclear medicine; Medical physics; Image quality; Medicine; Radiology; Computer science; Computer vision; Image (mathematics); Nuclear physics","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":[0.0006766833,0.0004403451,0.0004537414,0.0004472756,0.0001356611,0.0005558634,0.000717767,0.0004819076,0.001243775],"category_scores_gemma":[0.001903145,0.0002652705,0.0003991239,0.0003794341,0.000561891,0.0007610651,0.000552447,0.000407632,0.0002104249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006862097,"about_ca_system_score_gemma":0.0003691065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338419,"about_ca_topic_score_gemma":0.0008524445,"domain_scores_codex":[0.999605,0.00008063156,0.00001435996,0.00008390986,0.0001759985,0.00004002076],"domain_scores_gemma":[0.9991322,0.0004814859,0.00008468,0.00009707369,0.0001751983,0.00002939093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002448402,0.0001057835,0.002742247,0.0003748217,0.0001026177,0.0004776418,0.0002794703,0.6432921,0.1946469,0.07593908,0.001254597,0.08053994],"study_design_scores_gemma":[0.000003969629,0.00003448158,0.001302896,0.000006210465,0.000007290229,0.00007851059,0.00001240966,0.9825382,0.009394212,0.006033013,0.0005751445,0.00001357927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1360097,0.0005266228,0.8578268,0.0001709601,0.00002035027,0.0000526116,0.0001090688,0.0002898428,0.004994191],"genre_scores_gemma":[0.9067637,0.0004890074,0.08912685,0.0001249029,0.00002238749,0.00008415827,0.0001674564,0.00008895771,0.003132542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001338419,"threshold_uncertainty_score":0.004978836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01100275076211153,"score_gpt":0.2319972071748375,"score_spread":0.220994456412726,"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."}}