Development of a multi-layer silicon beta-ray spectrometer for beta spectrometry and dosimetry at CANDU power plants
Bibliographic record
Abstract
A compact multi-layer silicon beta-ray spectrometer has been developed for beta spectrometry and dosimetry for the beta-gamma mixed fields encountered at CANDU nuclear plants. Its design is based on the principle that the coincidence operations between silicon detectors make most gamma detection events be rejected while beta detection events are saved in the beta energy region of 0.7 to 3 MeV. A prototype spectrometer consists of a collimator, an entrance window, a stack of silicon detectors, an interface board, and a quad-input pulse processing system. Geant4 Monte Carlo simulations were carried out to optimize the configuration of the detector stack and compute the spectrometer responses to beta and gamma radiations, which led to an optimum detector stack consisting of a 500 μ m front detector followed by three 1500 μ m detectors. To characterize the gamma rejection and beta spectrometric performance, comprehensive measurements were carried out for various mixed beta-gamma fields with different beta count rates and beta-gamma count ratios that were created by varying the positions of a 90 Sr / 90 Y beta source and a 137 Cs gamma source. The coincidence spectra showed excellent gamma rejection performance in most energy regions above 250 keV while notable gamma perturbation events were identified in the low energy region for the coincidence spectrum between the first two detectors and the anti-coincidence spectrum of the front detector. A further study will be conducted soon to improve the spectrometer performance in the low energy region.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".