TU‐A‐BRB‐06: Characterization of a Commercial Photodiode Based Plastic Scintillation Detector Prototype
Bibliographic record
Abstract
Purpose: The purpose of this work is to characterize a commercial plastic scintillation detector (PSD) prototype that uses a photodiode to convert scintillation light to an electronic signal that can be measured with an electrometer. We tested the reproducibility of the detector as well as its accuracy. Methods: The detector consists of 3 mm of scintillating fiber 1 mm in diameter connected to a photodiode via approximately 2 m of optical fiber. We tested the reproducibility of the detector by making daily measurements in a cobalt beam. We then verified the accuracy of the detector by measuring percent depth dose (PDD) curves on a linac for two photon and electron energies. The measurements were compared to commissioning data (measured with an ionization chamber) to quantify the accuracy of the detector. Results: Over the course often days the mean dose the detector measured varied by 0.16% (1 standard deviation). The individual measurements on specific days varied as much as 0.27% and as little as 0.09%. PDD curves obtained with the detector differed from commissioning data by less than 0.6% for both photon energies. For both electron energies, the detector results differed from commissioning data less than 3% before the steep dose gradient towards the end of the electron range. In the dose falloff the difference was as great as 8% (likely due to positioning errors). Conclusions: This new photodiode based PSD exhibited a very high level of reproducibility both in terms of individual measurements and average measurements on different days. Depth dose curves measured with the PSD in photon and electron beams demonstrated excellent agreement with machine commissioning data, validating the accuracy of this detector as well confirming that it is an energy independent detector. This work was partly supported by an NIH/NCI SBIR phase I grant (1R43CA153824‐01) in collaboration with Standard Imaging Inc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".