{"id":"W2000322742","doi":"10.1118/1.2961507","title":"SU‐GG‐I‐109: Using EGSnrc Within GATE to Improve the Efficiency Of positron Emission Tomography Simulations","year":2008,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Imaging phantom; Monte Carlo method; Physics; Scanner; Positron emission tomography; Benchmark (surveying); Nuclear medicine; Medical physics; Computational physics; Computer science; Optics; Mathematics; Statistics; Medicine","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":[0.0002707377,0.0001329318,0.000255799,0.00004958814,0.0002074361,0.000006712949,0.0002250959,0.00008979229,0.00007046604],"category_scores_gemma":[0.0003526304,0.00008442812,0.0001213297,0.0006374107,0.0003797106,0.00003658998,0.00009807394,0.0003613635,0.000005533672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003205968,"about_ca_system_score_gemma":0.0002203107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009349076,"about_ca_topic_score_gemma":6.759796e-7,"domain_scores_codex":[0.9982862,0.0000316374,0.0003755083,0.000237719,0.0008386332,0.0002303229],"domain_scores_gemma":[0.9987571,0.0001513946,0.000109845,0.0004652352,0.0001278494,0.0003886126],"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":[0.0002132666,0.003742324,0.0189351,0.000419249,0.0001896994,0.00007758445,0.00450631,0.0007207696,0.9142205,0.005032233,0.02692526,0.02501774],"study_design_scores_gemma":[0.002271984,0.0008218944,0.002815349,0.001161333,0.0004126038,0.0001308655,0.0001168132,0.2710168,0.7060774,0.007074288,0.007507801,0.0005928745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.834771,0.0000410971,0.1583861,0.0056291,0.00009098146,0.0005403276,0.00001269869,0.0001045974,0.0004240829],"genre_scores_gemma":[0.9930573,0.0000104774,0.004962713,0.001507208,0.0002969673,0.0000180761,0.00001889079,0.00001962477,0.0001087098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.270296,"threshold_uncertainty_score":0.3442879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02601531634203884,"score_gpt":0.3284869333635068,"score_spread":0.302471617021468,"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."}}