A design methodology using signal‐to‐noise ratio for plastic scintillation detectors design and performance optimization
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: The design of novel plastic scintillation detectors (PSDs) is impeded by the lack of a suitable framework to simulate and predict their performance. The authors propose to use the signal-to-noise ratio (SNR) to model the performance of PSDs that use charge-coupled devices (CCDs) as photodetectors. METHODS: In PSDs using CCDs, the SNR is inversely related to the normalized standard deviation of the dose measurement. Thus, optimizing the SNR directly optimizes the system's precision. In this work, a model of SNR as a function of the system parameters is derived for optical fiber-based PSD systems. Furthermore, this proposed model is validated using experimental results. A formula for the efficiency of fiber coupling to CCDs is derived and used to simulate the performance of a PSD under varying magnifications. RESULTS: The proposed model is shown to simulate the experimental performance of an actual PSD to a suitable degree of accuracy under various conditions. CONCLUSIONS: The SNR constitutes a useful tool to simulate the dosimetric precision of PSDs. Using the SNR model, recommendations for the design and optimization of PSDs are provided. Using the same framework, recommendations for non-fiber-based PSDs are also provided.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it