Cross-jurisdictional analysis and forecasting of North American nuclear fuel inventory using a standardized unit
Bibliographic record
Abstract
This study fills the noted gap in comparative analyses of spent nuclear fuel (SNF) by assessing inventories from two key nuclear power regions, the USA and Canada, using a comprehensive analytical framework and standardized data from 2009 to 2021. In the USA, SNF inventory increased by 14.7 % in fuel assembly weight and 47 % in residual heavy metal content compared to Canada, in line with their use of light water reactors. Canada's SNF production is directly correlated to its nuclear power output, influenced by the lower burnup of natural uranium fuel used in CANDU reactors (R 2 = 0.57; p-value < 0.05) while the USA shows insignificant correlation, likely due to a variety of reactor types and higher burnup rates (R 2 = 0.008; p-value > 0.05). Further, the study identifies a strong negative correlation between uranium mine production and SNF inventory in the USA, indicating a reliance on imports amidst negligible domestic mining. In contrast, Canada also exhibits moderate negative dependency due to its position as a major uranium exporting jurisdiction. The obtained negative correlations with coal rents in both countries indicate a shift towards more nuclear energy use, impacting economic growth and energy patterns. The developed predictive models indicate a higher future SNF increase in Canada than in the USA. These findings are essential for planning the transition from temporary to permanent SNF disposal, ensuring safe long term management of radioactive waste.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".