Review of Methods and Results for Reactor Physics Analysis of Thorium-Based Fuels From Irradiation Experiments Conducted in the National Research Universal Reactor
Bibliographic record
Abstract
Abstract The development of advanced fuels and fuel cycles for conventional Generation III+ reactors, advanced reactors (such as Generation IV), and small modular reactors will help ensure the long-term sustainability and safety of the use of nuclear energy. Thorium-based fuels are an example of an advanced fuel that could augment and extend uranium resources. As part of previous efforts in Canada to investigate the use of thorium-based fuels for potential use in pressure-tube heavy-water reactors and other technologies, irradiation experiments with various thorium-containing fuels had been conducted in the National Research Universal (NRU) reactor at the Chalk River Laboratories. The NRU reactor physics computational tools and methods, developed initially for conventional enriched uranium fuel, had also been extended for analysis of thorium-based fuel experiments, with most satisfactory results. In this study, a number of physics and operation factors have been analyzed to quantify their effects on the production and burnup of 233U in thorium-based fuel, as well as to improve both the analysis and the use of thorium fuels and fuel cycles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".