Slope stability thresholds for vegetated hillslopes: a composite model
Bibliographic record
Abstract
Location of the critical shear plane (CSP) plays a major role in determining landslide-initiation thresholds. Depth to the CSP increases as the soil shear strength increases. Tree roots provide a significant strength contribution to soil shear strength. Our objective is to understand how vegetation can be used to increase landslide-initiation thresholds by changing the location of the CSP. This will enable us to select and compare combinations of plant species and densities to suit any given situation to increase landslide-initiation thresholds and improve slope stability. The CSP location is estimated incorporating available root cross-sectional area root depth data in the stability analysis in terms of energy. The energy approach has been developed to take into account the contribution of the roots to soil strength. Generalization of the original energy approach is required to enable its use outside our study areas. Once depth to the CSP is found, the time for the wetting front to reach it is found using a soil-water infiltration model. The composite model described may be used as a simple tool to choose the most appropriate plant density to maximize the stability of a given hillslope. A worked example of the model demonstrates how the approximate thresholds for different hillslopes with known plant densities under different climatic conditions are estimated.Key words: slope stability, safety factor, roots, energy, threshold.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".