Using Three-dimensional Plant Root Architecture in Models of Shallow-slope Stability
Bibliographic record
Abstract
BACKGROUND: The contribution of vegetation to shallow-slope stability is of major importance in landslide-prone regions. However, existing slope stability models use only limited plant root architectural parameters. This study aims to provide a chain of tools useful for determining the contribution of tree roots to soil reinforcement. METHODS: Three-dimensional digitizing in situ was used to obtain accurate root system architecture data for mature Quercus alba in two forest stands. These data were used as input to tools developed, which analyse the spatial position of roots, topology and geometry. The contribution of roots to soil reinforcement was determined by calculating additional soil cohesion using the limit equilibrium model, and the factor of safety (FOS) using an existing slope stability model, Slip4Ex. KEY RESULTS: Existing models may incorrectly estimate the additional soil cohesion provided by roots, as the spatial position of roots crossing the potential slip surface is usually not taken into account. However, most soil reinforcement by roots occurs close to the tree stem and is negligible at a distance >1.0 m from the tree, and therefore global values of FOS for a slope do not take into account local slippage along the slope. CONCLUSIONS: Within a forest stand on a landslide-prone slope, soil fixation by roots can be minimal between uniform rows of trees, leading to local soil slippage. Therefore, staggered rows of trees would improve overall slope stability, as trees would arrest the downward movement of soil. The chain of tools consisting of both software (free for non-commercial use) and functions available from the first author will enable a more accurate description and use of root architectural parameters in standard slope stability analyses.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".