Bibliographic record
Abstract
Near interfaces between two different media exposed to high-energy electron beams substantial dose and fluence perturbations due to backscatter can be observed. In this work, dose and fluence perturbations were studied for 4-19 MeV electron beams at backscatter interfaces of polystyrene, graphite, water, aluminium and lead. Measurements of relative dose using an NPL-designed thin-window plane-parallel ion chamber and a Markus ion chamber were performed to determine the effect of different interface materials and thicknesses. Results of Monte Carlo simulations with the EGSnrc code, including models of the ion chambers, were found to be in excellent agreement with the measurements. The well-documented increasing dose perturbation with increasing effective atomic number of the backscatter material and decreasing electron beam energy was confirmed. Simulations in a simplified slab geometry showed that, despite the decrease of average electron energy with depth in water, the dose perturbations decrease with increasing depth of the interface in water for all the materials in the study. This was ascribed to the change of the electron angular distribution with depth which has a different effect in water and in the presence of a high-Z interface. Electron fluence perturbations near a lead/water interface were found to cause small differences in unrestricted mass collision stopping power ratios, water to air. Effects of bremsstrahlung photons, characteristic photons and positrons from the backscattering material were found to be insignificant for electron interface dosimetry. When comparing simulations using EGSnrc and the older version of the same code, EGS4, underestimations of the dose perturbation effects of up to 7% were found when using the latter code to simulate 4 MeV electrons irradiating a lead/water interface. It is concluded that EGSnrc is a highly suitable tool for electron interface dosimetry studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".