Safety Function of Cementitious Materials and the Analytical Assessment of Long-Term Evolution of Cement-Bentonite Interface for Geological Disposal in Japan
Bibliographic record
Abstract
Cementitious materials used in geological disposal repositories are expected to have various functions for construction, operation and closure of the high-level radioactive waste (HLW)/TRans-Uranic (TRU) waste repositories and they also have functions for safety. In the long term after closure of the repositories, cementitious materials are expected to reduce the release of radionuclides from the waste. However, the expected performance of cementitious materials may decrease in the long term because of their gradual dissolution/alteration. In addition, there is a concern that the high pH groundwater due to alkaline ions leached from cementitious materials may degrade the safety functions of other components (buffer, backfill, host rock) of the repositories. Therefore, in order to understand how the expected safety functions of the cementitious materials and other components can be achieved in the post-closure period, NUMO carried out the analytical evaluation of the evolution of each component. The results showed that most of the cementitious materials and other components will remain during a long-term post-closure period. At present, we are aiming to improve the reliability of the analytical model and to develop a more realistic nuclide migration model that reflects the effect of cementitious materials on reducing mass transfer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".