Toxicity of Irradiated Advanced Heavy Water Reactor Fuels
Bibliographic record
Abstract
The good neutron economy and online refueling capability of the CANDU® heavy water moderated reactor (HWR) enable it to use many different fuels such as low enriched uranium (LEU), plutonium, or thorium, in addition to its traditional natural uranium (NU) fuel. The toxicity and radiological protection methods for these proposed fuels, unlike those for NU, are not well established. This study uses software to compare the fuel composition and toxicity of irradiated NU fuel against those of two irradiated advanced HWR fuel bundles as a function of post-irradiation time. The first bundle investigated is a CANFLEX® low void reactor fuel (LVRF), of which only the dysprosium-poisoned central element, and not the outer 42 LEU elements, is specifically analyzed. The second bundle investigated is a heterogeneous high-burnup (LEU,Th)O(2) fuelled bundle, whose two components (LEU in the outer 35 elements and thorium in the central eight elements) are analyzed separately. The LVRF central element was estimated to have a much lower toxicity than that of NU at all times after shutdown. Both the high burnup LEU and the thorium fuel had similar toxicity to NU at shutdown, but due to the creation of such inhalation hazards as (238)Pu, (240)Pu, (242)Am, (242)Cm, and (244)Cm (in high burnup LEU), and (232)U and (228)Th (in irradiated thorium), the toxicity of these fuels was almost double that of irradiated NU after 2,700 d of cooling. New urine bioassay methods for higher actinoids and the analysis of thorium in fecal samples are recommended to assess the internal dose from these two fuels.
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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 | 0.000 | 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) | 0.002 | 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 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".