Shallow landsliding, root reinforcement, and the spatial distribution of trees in the Oregon Coast Range
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The influence of root reinforcement on shallow landsliding has been well established through mechanistic and empirical studies, yet few studies have examined how local vegetative patterns influence slope stability. Because root networks spread outward from trees, the species, size, and spacing of trees should influence the spatial distribution of root strength. We documented the distribution and characteristics of trees adjacent to 32 shallow landslides that occurred during 1996 in the Oregon Coast Range. Although broadly classified as a conifer-dominated forest, we observed sparse coniferous and abundant hardwood trees near landslide scars in an industrial forest (Mapleton) that experienced widespread burning in the 19th century. In industrial forests that were burned, selectively harvested, and not replanted (Elliott State Forest), swordfern was ubiquitous near landslides, and we observed similar numbers of live conifer and hardwood trees proximal to landslide scarps. We demonstrate that root strength quantified in landslide scarps and soil pits correlates with a geometry-based index of root network contribution derived from mapping the size, species, condition, and spacing of local trees, indicating that root strength can be predicted by mapping the distribution and characteristics of trees on potentially unstable slopes. In our study sites, landslides tend to occur in areas of reduced root strength, suggesting that to make site-specific predictions of landslide occurrence slope stability analyses must account for the diversity and distribution of vegetation in potentially unstable terrain.Key words: slope stability, vegetation, root strength, shallow landslide, debris flow, Oregon Coast Range.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it