Projecting thyroid cancer risk to the general public from radiation exposure following hypothetical severe nuclear accidents in Canada
Bibliographic record
Abstract
A robust emergency management framework can limit radiation exposures and effectively minimize the potentially devastating consequences of a nuclear emergency. We projected thyroid cancer risk resulting from two hypothetical severe nuclear accidents occurring at the Darlington nuclear power plant (NPP) located in Ontario, Canada. Single- and multi-unit accidents were considered. A dose assessment was previously performed using the MELCOR Accident Consequences Code System. The generic criteria used to select the appropriate protective actions are described in the Ontario Provincial Nuclear Emergency Response Plan (PNERP). We hypothesized protection actions only within the 10 kilometre (km) radius of the NPP given the time sensitivity of iodine thyroid blocking. The excess risk of developing thyroid cancer was projected using the US National Cancer Institute's radiation risk assessment tool RadRAT. We projected zero dose, and subsequently zero risk of developing thyroid cancer for people living within 10 km of the NPP, due to effective implementation of selected protective actions. Based on centreline doses, at the 12 km radius, excess childhood thyroid cancer risks of approximately 600% and 130% compared to the baseline were projected for the single- and multi-unit scenarios, respectively. The risk of developing thyroid cancer was projected to be low for adults for both scenarios. The results of this modelling study provide insights into the effectiveness of protective actions in reducing radiation-related thyroid cancer risk when considering hypothetical severe nuclear accidents. Implementation of select protective actions protects the population living near the Darlington NPP. The projected increase of developing thyroid cancer for children living beyond 10 km could potentially be eliminated with additional mitigation measures specified in the PNERP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".