Investigating electrical resistivity of highly compacted bentonite as a monitoring option for nuclear waste repositories
Bibliographic record
Abstract
Bentonite clay is an essential component of the engineered barrier systems required around nuclear waste bundles in a deep geological repository (DGR). The bentonite is subject to both thermal and hydraulic gradients which may cause failure of the barrier system compromising the stability of the deep geological repository. Regular monitoring and evaluation of the condition of the highly compacted bentonite (HCB) is the key to the long term safe storage of nuclear waste bundles. The degree of saturation of the bentonite is the most critical parameter used to assess the performance of the material. The thermal and electrical resistivity of highly compacted bentonite samples were measured to develop a relationship based on the degree of saturation of the material. In addition, this research investigated the relationship between total suction and electrical resistivity. Moreover, estimating the microbial activity near the waste bundles is a crucial part of the monitoring-process, because it can lead to microbial induced corrosion (MIC) and jeopardize the safety of the repository. The inherent physical characteristics of highly compacted bentonite, such as high swelling pressure and small pore size, reduce the microbial activity near the used fuel containers, which would reduce or eliminate the possibility of microbial induced corrosion. It is reported in the literature that the microbial activity in the bulk of compacted bentonite can be controlled if the emplaced compacted bentonite has a uniform dry density of more than 1.6 g/cm3 and salt concentrations above 100 g/L for sodium and calcium chloride; these conditions ensure that the swelling pressure is higher than 2 MPa and it keeps the water activity and the average pore size lower than 0.96 and 0.02 μm, respectively. High salinity typically plays a vital role in suppressing the microbial activity in the compacted bentonite. As mentioned above, the electrical resistivity is a powerful tool to monitor the performance of repositories. Following the same procedure, the salinity of the highly compacted bentonite can be monitored by means of electrical resistivity to obtain a clear depiction of the microbial activity within the deep geological repository.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".