Sci‐YIS Fri ‐ 09: Small volume dosimetry with multiple scintillation probes
Bibliographic record
Abstract
We present a novel matrix dosimeter with high sensitivity, good precision and reproducibility based on plastic scintillation dosimetry. This dosimeter possesses excellent water equivalence and linearity to dose, dose rate and energy. It was built by coupling scintillating fibers, chosen for their high collection efficiency, to clear optical fiber. Light measurements were performed with a color CCD camera in order to compare two techniques to remove the stem effect: background subtraction and chromatic filtering. Chromatic filtering uses light at two different wavelengths (green and blue channels of the CCD) to remove the undesired Cerenkov radiation. Irradiations were performed at 6 MV for various doses and field sizes. Stem effect caused by Cerenkov radiation ranged from 5 % (5×5 field) to 35 % (30×30 field) of the total signal. Chromatic filtering and background subtraction both allow to extract from the signal the stem effect with a similar precision. Linearity of the system was validated down to 2.5 cGy (1.31 % standard deviation) with 0.0055 cm2 probes. At 10 cGy the standard deviation dropped below 0.6 %. Depth dose curves were also measured with a precision below 1 % compared to ionization chamber measurements. Chromatic filtering removes the necessity of a second optical fiber for background subtraction, therefore increasing the spatial resolution while maintaining a precision below 1 % for most of the dose range. The CCD camera allows more than 150 detectors in its field of view, which can be used in water, in phantom or in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".