Sci-Thur PM: YIS - 10: A new optically encoded single-fiber plastic scintillation detector for multi-point radiation dosimetry
Bibliographic record
Abstract
PURPOSE: To develop a new multi-point plastic scintillation detector (mPSD) that allows for simultaneous dose measurements at multiple points and uses a single optical guide. MATERIALS AND METHODS: Two different prototypes were built. A two-point mPSD was built and light discrimination was based on the use of multiple color filters at the outputs of a network of optical fiber splitters. Light intensity was measured by an EMCCD camera. For the three-point mPSD, the light discrimination setup was replaced by a low-noise spectrometer. Depth-dose and profiles measurements were obtained on a 6 MV photon beam with the mPSDs inside a water phantom. An ion chamber was also used for comparison purpose. Finally, the three-point mPSD was tested under an Ir-192 high-dose-rate (HDR) brachytherapy dose delivery and compared to the treatment planning system. RESULTS: A good agreement was found between the measured and expected dose for both mPSDs. The average relative differences to the ion chamber measurement for the two-point mPSD were of (2.4 ± 1.6)% and (1.3 ± 0.8)%. For the three-point mPSD, these differences were of (2.3±1.1)%, (1.6±0.4)% and (0.32±0.19)%. The latter mPSD was shown very versatile, being able to measure dose from HDR brachytherapy with an average accuracy of (2.3±1.0)% per catheter. CONCLUSIONS: The practical feasibility of mPSDs using a single optical guide has been demonstrated under irradiation from a 6 MV photon beam and an Ir-192 HDR brachytherapy source. Their application for pre-treatment quality assurance and in vivo dosimetry will be various.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.003 |
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".