Hydraulic conductivity of model soil–bentonite backfills subjected to wet–dry cycling
Bibliographic record
Abstract
The potential for changes in hydraulic conductivity, k, of two model soil-bentonite (SB) backfills subjected to wet-dry cycling was investigated. The backfills were prepared with the same base soil (clean, fine sand) but different bentonite contents (2.7 and 5.6 dry wt %). Saturation (S), volume change, and k of consolidated backfill specimens (effective stress = 24 kPa) were evaluated over three to seven cycles in which the matric suction, Ym, in the drying stage ranged from 50 to 700 kPa. Both backfills exhibited susceptibility to degradation in k caused by wet-dry cycling. Mean values of k for specimens dried at Ym = 50 kPa (S = 30-60 % after drying) remained low after two cycles, but increased by 5- to 300-fold after three or more cycles. Specimens dried at Ym ≥ 150 kPa (S < 30 % after drying) were less resilient and exhibited 500- to 10 000-fold increases in k after three or more cycles. The greater increases in k for these specimens correlated with greater vertical shrinkage upon drying. The findings suggest that increases in hydraulic conductivity due to wet-dry cycling may be a concern for SB vertical barriers located within the zone of a fluctuating groundwater table.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".