{"id":"W2017159023","doi":"10.1088/0031-9155/56/22/010","title":"A GPU implementation of EGSnrc's Monte Carlo photon transport for imaging applications","year":2011,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; University of Manitoba","funders":"CancerCare Manitoba Foundation","keywords":"Monte Carlo method; CUDA; Photon; Computer science; Graphics; Range (aeronautics); Graphics processing unit; Computational science; Physics; Computational physics; Parallel computing; Optics; Computer graphics (images); Statistics; Mathematics","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":[0.0001790211,0.0000777167,0.0002464966,0.00005780728,0.00002426452,4.356646e-7,0.00005336343,0.0000319736,0.00003127847],"category_scores_gemma":[0.000009639479,0.00005862494,0.00003216944,0.0001282845,0.0002219154,0.00002117499,0.0000105319,0.00008265099,2.815838e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001046921,"about_ca_system_score_gemma":0.00002873289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000796657,"about_ca_topic_score_gemma":0.00002468442,"domain_scores_codex":[0.9993794,0.000009342973,0.000266413,0.0001746982,0.00004286476,0.0001273099],"domain_scores_gemma":[0.9996025,0.00004697444,0.00008008747,0.0001468723,0.00006945327,0.00005411846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001595355,0.0005257782,0.3990569,0.0006017826,0.00008880752,0.000003286149,0.00432063,2.582277e-7,0.1537248,0.1097317,0.002927065,0.3288594],"study_design_scores_gemma":[0.02422872,0.004231612,0.2110397,0.001219419,0.00182218,0.0001252984,0.01346488,0.00812267,0.1766467,0.3758463,0.1820116,0.001240952],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6813259,0.0007598415,0.3025075,0.008768391,0.00007357436,0.003332385,0.00008455119,0.0001113833,0.003036463],"genre_scores_gemma":[0.985867,0.0002718126,0.01248311,0.0006295028,0.0001273729,0.0005241106,0.00007761634,0.000008215263,0.00001121377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3276185,"threshold_uncertainty_score":0.2390656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2211794675683363,"score_gpt":0.4579854881756671,"score_spread":0.2368060206073308,"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."}}