Achieving Resource Sustainability in China Through the Thorium Fuel Cycle in the Candu Reactor
Bibliographic record
Abstract
The CANDU® reactor is the most resource-efficient reactor commercially available. The features that enable the CANDU reactor to utilize natural uranium facilitate the use of a wide variety of thorium fuel cycles. In the short term, the initial fissile material would be provided in a “mixed bundle”, in which low-enriched uranium (LEU) would comprise the outer two rings of a CANFLEX® bundle, with ThO2 in the central 8 elements. This cycle is economical, both in terms of fuel utilization and fuel cycle costs. The medium term strategy would be defined by the availability of plutonium and recovered uranium from reprocessed used LWR fuel. The plutonium could be used in Pu/Th bundles in the CANDU reactor, further increasing the energy derived from the thorium. Recovered uranium could also be effectively utilized in CANDU reactors. In the long term, the full energy potential from thorium could be realized through the recycle of the U-233 (and thorium) in the used CANDU fuel. Plutonium would only be required to top up the fissile content to achieve the desired burnup. Further improvements to the CANDU neutron economy could make possible a very close approach to the Self-Sufficient Equilibrium Thorium (SSET) cycle with a conversion ratio of unity, which would be completely self-sufficient in fissile material (recycled U-233).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".