Bibliographic record
Abstract
With world stockpiles of used nuclear fuel increasing, the need to address the long-term utilization of this resource is being studied. Many of the transuranic (TRU) actinides in nuclear spent fuel produce decay heat for long durations, resulting in significant nuclear waste management challenges. These actinides can be transmuted to shorter-lived isotopes to reduce the decay heat period or consumed as fuel in a CANDU® reactor. Many of the design features of the CANDU reactor make it uniquely adaptable to actinide transmutation. The small, simple fuel bundle simplifies the fabrication and handling of active fuels. Online refuelling allows precise management of core reactivity and separate insertion of the actinides and fuel bundles into the core. The high neutron economy of the CANDU reactor results in high TRU destruction to fissile-loading ratio. This paper provides a summary of actinide transmutation schemes that have been studied in CANDU reactors at AECL, including the works performed in the past [1–4]. The schemes studied include homogeneous scenarios in which actinides are uniformly distributed in all fuel bundles in the reactor, as well as heterogeneous scenarios in which dedicated channels in the reactor are loaded with actinide targets and the rest of the reactor is loaded with fuel. The transmutation schemes that are presented reflect several different partitioning schemes. Separation of americium, often with curium, from the other actinides enables targeted destruction of americium, which is a main contributor to the decay heat 100 to 1000 years after discharge from the reactor. Another scheme is group-extracted transuranic elements, in which all of the transuranic elements, plutonium (Pu), neptunium (Np), americium (Am), and curium (Cm) are extracted together and then transmuted. This paper also addresses ways of utilizing the recycled uranium, another stream from the separation of spent nuclear fuel, in order to drive the transmutation of other actinides.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".