Eye lens dosimetry in Canadian CANDU nuclear power plants based on operational dosimetric quantities H<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1219" altimg="si24.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>(10) and H<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1227" altimg="si24.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>(0.07)
Bibliographic record
Abstract
In this article, we present a methodology for performing eye lens dosimetry in CANDU nuclear power plants using an existing and highly accurate Harshaw 4-element TLD-700 dosemeter. This dosemeter, which has been specially designed for Ontario Power Generation (OPG) and Bruce Power (BP), measures the deep and shallow personal dose equivalent quantities Hp(10) and Hp(0.07), respectively. Using these measured personal dose equivalent quantities and applying a beta-ray strength scaling factor to the Hp(0.07) measurement in particular, we have developed an algorithm that can be used to calculate the dose to the lens of the eye in mixed beta–gamma fields. This scaling factor has been developed and is primarily based on results obtained from extensive collaborative study, performed by Ontario Power Generation (OPG), Bruce Power (BP) and McMaster University, through Candu Owners Group (COG) support (Bohra et al., 2021; Laranjeiro et al., 2020). Furthermore, scaling factor F, also includes effects of protective glass eyewear and results from Whole body dosimetry intercomparison exercises. The algorithm to calculate eye lens dose at CANDU power plants has been developed, based on this scaling factor and operational dosimetric quantities Hp(10) and Hp(0.07)
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.204 | 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; both teacher heads agree on what is shown here.
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".