Quantifying reinforcement of forested (<i>Cunninghamia lanceolata</i>) slopes with different gradients based on in-situ pullout experiments
Bibliographic record
Abstract
Tree roots play a crucial role in hillslope stability, but quantifying their reinforcement remains challenging. This study aims to quantify the root reinforcement provided by Cunninghamia lanceolata across varying slope gradients based on in-situ pullout experiments. A total of 120 soil profiles were excavated to map root distribution across four slope gradients. Subsequently, 304 in-situ pullout experiments were conducted encompassing root diameters ranging from 1 to 8 mm. The Root Bundle Model Weibull was calibrated and coupled with root distribution data to quantify reinforcement contributions from a single tree to stands. It was found slope gradient significantly influences root distribution, with steeper slopes harboring coarser and more widely distributed roots. In-situ experiments revealed substantial variability in pullout stiffness and peak displacement for roots of the same diameter, with thicker roots exhibiting higher stiffness and greater displacement. Calculations indicate that root reinforcement exhibits an exponential decline with increasing distance from the stem but shows a marked positive association with slope gradient due to the influence on root distribution. Statistical analysis reveals that the area experiencing root reinforcement exceeding 10 kPa on a 40° slope is roughly double that of 0° and 20° stands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".