Sci‐Sat AM: Radiation Dosimetry and Practical Therapy Solutions ‐ 10: Towards LET detection: A study on the effects of scintillator doping
Bibliographic record
Abstract
Purpose: In radiotherapy, the amount of radiation delivered is determined by optimizing the amount of absorbed dose to the tumor. Dose does not always correlate well with the actual biological effects of radiation. This work seeks to validate the LET‐dependence of doped plastic scintillators for use in a radiation beam quality (LET) detector. Methods: The LET spectrum ([Φ]) can be resolved knowing the measured signals of uniquely LET‐dependent detectors, [S], and the response of each LET‐dependent detector to specific LETs ([R]), through the relation [Φ]=[S][R]−1. Plastic scintillator response is intrinsically LET dependent and can be varied via doping. Initial prototype consists of plastic scintillator and glass taper coupled to an optical fiber; components are housed in black acrylic, reducing effect of ambient light. In order to determine [R], the light response matrix, GEANT4.10.1 Monte Carlo (MC) was used. To validate MC, measurements were done using high energy electrons (9,12,15MeV) and orthovoltage x‐rays (100,250kV); scintillator signal was normalized to dose measured simultaneously. Results: Stopping power was varied by changing particle type/energy; measurements indicated that as stopping power increased from 1.9 to 6.6MeV/cm, detector response increased by 263% (+/−29.2%) for 5%Pb‐doped scintillator (155% in MC); 52% (+/−7.8%) increase observed when undoped scintillator was used (49% in MC). 5%Pb‐doped discrepancy (100kV x‐rays) is being investigated. Conclusions: This work validates that doping effects LET/energy response of scintillators; an effect that can be utilized for construction of an LET detector.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".