A three-dimensional study of vegetation management on cut slopes
Bibliographic record
Abstract
Infrastructure slopes often become covered in dense vegetation due to poor vegetation management. Despite increasing cohesion and enhancing slope stability, high water demand vegetation leads to serviceability problems, primarily towards the end of the summer. Drastic approaches, however, such as vegetation clearance, have caused instabilities during wet seasons. Therefore, appropriate, effective, and continuous vegetation management is of essence and should consider both biodiversity and the engineering asset, while accounting for the contribution of vegetation in battling climate change. Developing numerical methodologies and models can be particularly useful in acquiring insight into the complex mechanism and processes taking place during slope–plant–atmosphere interactions. The work presented here focused for the first time on combining three-dimensional (3D) stability and serviceability issues through the development of a 3D numerical model to investigate different vegetation management strategies for a slope covered in high evapotranspiration demand vegetation and suffering serviceability problems. Different 3D patterns of vegetation removal and of replacement with lower water demand vegetation were considered and the effect of each of these on the serviceability and stability of the slope during the subsequent year was examined. The results demonstrated that replacement was preferable to removal, as stability and serviceability should be considered concurrently, and that, occasionally, clearance may have detrimental effects not only on stability but also on serviceability. The importance of considering out-of-plane displacements, which have traditionally been ignored, was revealed, thus providing numerical evidence that a shift in field monitoring is required, to capture the three-dimensionality of the problem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".