Influence of lubricant fluids on swelling behaviour of Queenston shale in southern Ontario
Bibliographic record
Abstract
The feasibility of using the microtunnelling technique to install pipelines through Queenston shale of southern Ontario is being investigated. In the microtunnelling technique, lubricant fluids — such as bentonite slurry and polymer solution — are used to facilitate excavation during installation of the pipeline sections. In this regard, a comprehensive testing program was performed to investigate the time-dependent deformation behaviour of Queenston shale considering lubricant fluids used in construction. The free swell test, semi-confined swell test, and the null swell test were utilized to perform this study. Results of 144 tests are presented and the variation of swelling characteristics of Queenston shale in lubricant fluids and in water is discussed briefly. The swelling model suggested by Lo and Hefny in 1996 was adopted to develop the swelling envelopes of Queenston shale in lubricant fluids and water in both horizontal and vertical directions with respect to the rock bedding. In comparison to swelling in fresh water, the study revealed that the polymer solution has substantially reduced the swelling of Queenston shale in all directions, while the bentonite solution was less efficient in reducing the swelling of Niagara Queenston shale, and has a slight negative influence on the swelling (i.e., increased swelling) of Milton Queenston shale.
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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 | 0.001 | 0.001 |
| 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".