Monte Carlo study of Si diode response in electron beams
Bibliographic record
Abstract
Silicon semiconductor diodes measure almost the same depth-dose distributions in both photon and electron beams as those measured by ion chambers. A recent study in ion chamber dosimetry has suggested that the wall correction factor for a parallel-plate ion chamber in electron beams changes with depth by as much as 6%. To investigate diode detector response with respect to depth, a silicon diode model is constructed and the water/silicon dose ratio at various depths in electron beams is calculated using EGSnrc. The results indicate that, for this particular diode model, the diode response per unit water dose (or water/diode dose ratio) in both 6 and 18 MeV electron beams is flat within 2% versus depth, from near the phantom surface to the depth of R50 (with calculation uncertainty <0.3%). This suggests that there must be some other correction factors for ion chambers that counter-balance the large wall correction factor at depth in electron beams. In addition, the beam quality and field-size dependence of the diode model are also calculated. The results show that the water/diode dose ratio remains constant within 2% over the electron energy range from 6 to 18 MeV. The water/diode dose ratio does not depend on field size as long as the incident electron beam is broad and the electron energy is high. However, for a very small beam size (1 X 1 cm(2)) and low electron energy (6 MeV), the water/diode dose ratio may decrease by more than 2% compared to that of a broad beam.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".