Extraction of depth-dependent perturbation factors for silicon diodes using a plastic scintillation detector
Bibliographic record
Abstract
PURPOSE: This work presents the experimental extraction of the perturbation factor in megavoltage electron beams for three models of silicon diodes (IBA Dosimetry, EFD and SFD, and the PTW 60012 unshielded) using a plastic scintillation detector (PSD). METHODS: The authors used a single scanning PSD mounted on a high-precision scanning tank to measure depth-dose curves in 6-, 12-, and 18-MeV clinical electron beams. They also measured depth-dose curves using the IBA Dosimetry, EFD and SFD, and the PTW 60012 unshielded diodes. The authors used the depth-dose curves measured with the PSD as a perturbation-free reference to extract the perturbation factors of the diodes. RESULTS: The authors found that the perturbation factors for the diodes increased substantially with depth, especially for low-energy electron beams. The experimental results show the same trend as published Monte Carlo simulation results for the EFD diode; however, the perturbations measured experimentally were greater. They found that using an effective point of measurement (EPOM) placed slightly away from the source reduced the variation of perturbation factors with depth and that the optimal EPOM appears to be energy dependent. CONCLUSIONS: The manufacturer recommended EPOM appears to be incorrect at low electron energy (6 MeV). In addition, the perturbation factors for diodes may be greater than predicted by Monte Carlo simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".