A Transient Seepage–Thermal Stability Model for Cohesionless Soil Slopes in the Semi-Arid Continental Climate of the Canadian Prairies
Bibliographic record
Abstract
Seasonal weather variations have a profound effect on the integrity of cohesionless soil slopes in the Canadian Prairies owing to the prevalent semi-arid continental climate. The primary contribution of this research is the development of a transient and two-dimensional stability model that is fully coupled with hydraulic and thermal flows. The model was used to predict the factor of safety (FS) for two slope geometries (18 m height and 26 m height) subjected to three weather scenarios (mean, extreme wet, and extreme dry) and four ponding combinations on the upstream and downstream sides of the slopes under no-load and train-load conditions. Results indicated that for mean climate conditions, FS trends fluctuate till April, followed by an increase that remains constant up to November and decreases thereafter. Generally, the FS shows subdued fluctuations and higher values for the high slope compared with the low slope. For wet climate conditions, the FS patterns are similar to mean conditions, albeit with reduced durations of stability during summer. For dry climate conditions, FS values are higher than mean conditions and nearly constant for most of the year. For no ponding and downstream ponding, FS > 1 throughout the year. In contrast, FS > 1 only during the summer (with reduced time in wet climate and extended time in dry climate conditions) for upstream ponding and upstream–downstream ponding. For train loading, FS shows subdued fluctuations and lower values than the corresponding no-loading scenarios for both slopes. The effects of climate conditions and ponding scenarios are further reduced for the high slope. These findings are useful for decision making with regard to the initial design and ongoing performance of natural slopes and embankments in the cohesionless soils of the region.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".