Sealing materials for a deep geological repository: evaluation of swelling pressure and hydraulic conductivity data for bentonite-based sealing materials proposed for use in placement rooms
Bibliographic record
Abstract
Several countries are planning to safely contain and isolate used nuclear fuel in a deep geologic repository. Similar to other countries, Canada's Nuclear Waste Management Organization (NWMO) proposes to place used fuel in corrosion-resistant containers deep underground as part of a multiple barrier approach. Key components of the sealing systems are bentonite-based and must maintain low hydraulic conductivity and an ability to swell when in contact with free groundwater. Swelling pressure ( Ps) and hydraulic conductivity ( k) data for the specific bentonite product known as MX80 bentonite under low (<15 g/L) through high (>325 g/L) total dissolved solids’ (TDS) pore fluids that simulate groundwaters that could be encountered in sedimentary and crystalline rock geospheres are reviewed, and information from ongoing NWMO studies of behaviour is presented. These data are statistically evaluated to establish the best-fit and prediction limit relationships between compacted dry density and Ps and k as they are influenced by porefluid composition. They also allow identification of dry density and(or) TDS (salinity) conditions where Ps and k may not meet defined performance. From these analyses, it is expected that bentonite can be placed such that on achieving fluid saturation and density equilibration, it meets the requirements set for placement room fill.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".