Leaching Impact on Compressibility and Shear Strength of Undisturbed and Cement-treated Champlain Sea Clay
Bibliographic record
Abstract
This research investigated the impact of leaching on the compressibility and shear strength properties of undisturbed and cement-treated Champlain Sea clay. A total of five undisturbed clay samples were leached with distilled water in the laboratory to reduce the salinity from initial values ranging from 9.5 to 15 g/L to the salinity values of 2.75, 1.45, 1.03, 0.55, and 0.35 g/L. A series of geotechnical tests were conducted on these samples at different salinity levels, including constant rate of strain consolidation tests, consolidated isotropic undrained triaxial compression tests, and vane shear tests. The experimental results showed that leaching leads to an increase in the compressibility and a reduction in shear strength of undisturbed Champlain Sea clay. The experimental results revealed that cement, mixed at a dosage of 50 kg/m3 , can significantly decrease the compressibility and increase the shear strength of Champlain Sea clay. A leached cement-treated sample exhibits a relatively higher compressibility than that of unleached cementtreated one. An increase in compressibility was also observed as salinity declines for the cementtreated samples. Moreover, a cement-treated sample at a lower salinity level displays slightly a higher shear strength compared to that of a cement-treated sample at the original salinity level.
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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 | 0.000 | 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".