Technology Perspectives on the Management of Spent-Resin Wastes Generated From Nuclear Power Reactor Operations
Bibliographic record
Abstract
Organic-resin wastes (spent resins) are generated by different purification systems employed in all types of nuclear power reactors during routine and non-routine operations. The quantities of such resin wastes, and their inventories of contaminants vary depend on the operational goals of the individual power plant. Depending on the regulatory target in the particular jurisdiction where the reactor is located, the type and amounts of radionuclides, metals and other chemical contaminants in the resin waste determine the extent of treatment required for interim storage or final disposal of the waste. Resin-waste treatment comprises different operations such as pretreatment, conditioning/stabilization and containerization that produce a waste package suitable for handling, transport, storage and disposal. One aspect of the contaminants that has significant impact on waste conditioning and the overall cost of managing such wastes are the concentrations of short half-life (arbitrarily less than approximately 30 years) radionuclides, and long half-life radionuclides, in particular carbon-14, and toxic metals present in the waste. A spectrum of resin-waste conditioning methods is available. Some methods have been applied to specific situations while others are being developed for future applications to meet the need for reducing worker dose, environmental releases, and waste-storage and disposal costs. This paper describes waste treatment options for low-level radioactive resin wastes and potential options of resin wastes containing appreciable amounts of carbon-14. Indications are that drying of the resin waste containing long half-life radionuclides such as carbon-14 and compaction or pelletizing can be favourable to allow interim dry-storage of the waste and to provide sufficient flexibility in the preparation of a suitable waste form to meet applicable waste acceptance criteria for the eventual disposal of such wastes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".