{"id":"W2026359758","doi":"10.1118/1.2874552","title":"Monte Carlo simulation of backscatter from lead for clinical electron beams using EGSnrc","year":2008,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Grand River Hospital; Toronto Metropolitan University","funders":"National Research Council Canada; McGill University; Varian Medical Systems","keywords":"Monte Carlo method; Electron; Materials science; Beam (structure); Backscatter (email); Cathode ray; Dosimetry; Atomic physics; Physics; Optics; Nuclear medicine; Nuclear physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008051136,0.000551231,0.0006484027,0.000644106,0.0004077309,0.0006037327,0.0006345949,0.001245909,0.001863289],"category_scores_gemma":[0.002487655,0.000669066,0.0006742143,0.0009269101,0.0003401426,0.0003168387,0.0003084557,0.0004980182,0.0003588971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105797,"about_ca_system_score_gemma":0.001201832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01611904,"about_ca_topic_score_gemma":0.01100542,"domain_scores_codex":[0.9997612,0.0001019952,0.00001179601,0.00001694003,0.00007610968,0.00003197144],"domain_scores_gemma":[0.9984968,0.001149949,0.00009260477,0.00004307926,0.0001783431,0.00003930286],"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":[0.00005520523,0.00001391739,0.0006243259,0.00002620187,0.00001403398,0.00004747474,0.00002571359,0.9960881,0.0008109093,0.0007712392,0.0001605073,0.001362431],"study_design_scores_gemma":[0.00001020556,0.00001528482,0.0001550768,0.000005613938,0.000005583439,0.00001491524,0.000006457333,0.9985281,0.0007308797,0.0002631539,0.0002584186,0.000006261026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6489132,0.001704653,0.3284651,0.0004497379,0.0001306507,0.0002373275,0.001298136,0.001526568,0.01727465],"genre_scores_gemma":[0.9270545,0.0005091267,0.06700286,0.0001721898,0.00001646931,0.0002546089,0.0006696922,0.0003253243,0.003995339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01611904,"threshold_uncertainty_score":0.03205043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05395920824694834,"score_gpt":0.3893868492962697,"score_spread":0.3354276410493214,"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."}}