Evaluation of the impact of pore fluid chemistry on the hydromechanical behaviour of clay-based sealing materials
Bibliographic record
Abstract
The results of an experimental program undertaken to evaluate the impact of pore fluid salinity on the hydromechanical performance of light and dense backfill materials are presented. Light and dense backfills are engineered barrier materials that are being examined in the Canadian concept for storage of spent fuel in a deep geological repository. The current research investigates the impact of pore fluid chemistry on the swelling, compressibility, stiffness, and hydraulic conductivity parameters of light and dense backfills that are required as material parameters for analysis and design. In these tests, pore fluid chemistry was selected to represent groundwater within potential host units including granite and limestone rock. Results show that the performance of light backfill is significantly affected by changes in pore fluid chemistry. The swell potential of light backfill decreases with increasing salinity of the solution. The hydraulic conductivity decreases with increasing effective montmorillonite dry density and specimens saturated with saline solution have higher hydraulic conductivity than those saturated with distilled water. Conversely, the behaviour of dense backfill is governed mainly by the crushed granite component and therefore changes to the pore fluid chemistry have relatively little effect. Results of dense backfill tests confirm the material performance as a sealing material.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".