{"id":"W2051874969","doi":"10.1118/1.4815527","title":"WE‐C‐108‐04: A Monte Carlo Investigation of Low‐Z Targets in a TrueBeam Linear Accelerator Using Varian Virtualinac","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Truebeam; Monte Carlo method; Linear particle accelerator; Imaging phantom; Physics; Photon; Nuclear medicine; Detector; Range (aeronautics); Optics; Computational physics; Materials science; Beam (structure); Medicine; Mathematics; Statistics","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":[0.000513827,0.0002515643,0.0002596084,0.0002766264,0.0003207585,0.0004822798,0.0007074262,0.0004553057,0.002401451],"category_scores_gemma":[0.001255576,0.0003175412,0.0003066638,0.0003683793,0.0002131248,0.0002856881,0.0002350798,0.0002812347,0.0002767663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008821405,"about_ca_system_score_gemma":0.000873356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01153931,"about_ca_topic_score_gemma":0.01094216,"domain_scores_codex":[0.9997991,0.00007811819,0.000007222288,0.00001779563,0.00007422594,0.00002354276],"domain_scores_gemma":[0.9989323,0.0007073084,0.00007165928,0.00007060316,0.0001798498,0.00003828442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001829156,0.00006852026,0.006580973,0.00006883076,0.0000458986,0.0001212639,0.00009620727,0.9748498,0.008354704,0.001924376,0.0007658764,0.006940648],"study_design_scores_gemma":[0.00001544156,0.00005582322,0.001377302,0.00000472482,0.000007405478,0.00004166275,0.00001380407,0.9925559,0.005025171,0.0001419607,0.0007521728,0.00000863229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8714465,0.0003594088,0.1080245,0.0002345208,0.00002443681,0.0001294821,0.0007577484,0.001923816,0.01709961],"genre_scores_gemma":[0.96062,0.00008622297,0.03674462,0.00004462792,0.000003852254,0.00007646979,0.0003408435,0.0002451017,0.001838101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01153931,"threshold_uncertainty_score":0.02294427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639411359083659,"score_gpt":0.2788589416468129,"score_spread":0.2624648280559763,"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."}}