Condition Monitoring of Radioactive Waste Packages: Experience with the 4 Metre Box
Bibliographic record
Abstract
Abstract In the United Kingdom, most intermediate level radioactive waste is being packaged in grade 316L stainless steel containers of various sizes. There is a need to ensure that the containers maintain their integrity during storage for a period that may extend for several decades. It is therefore important to have methods available for monitoring the condition of such packages so that at the end of the storage period the containers are in a form which is suitable for safe storage, transport, handling and potential disposal. A prototype container, the 4 Metre Box, which is fabricated in grade 304L stainless steel, has been used to gain experience in applying condition monitoring and corrosion monitoring techniques. The programme involved two main themes: characterising the environmental conditions (air temperature, surface temperature, relative humidity, time of wetness, surface chloride) and monitoring any changes in the condition of the box (surface strain, visual inspection, potential of reinforcement in concrete liner, dye penetrant). Atmospheric corrosion probes and corrosion coupons were also employed. This paper presents the results acquired during a five-year monitoring period and demonstrates the importance of environmental conditions, surface treatment and surface cleanliness in ensuring good corrosion resistance of stainless steel radioactive waste containers during storage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".