Size distribution and size of loess slides in response to slope height and slope gradient based on field survey data
Bibliographic record
Abstract
This paper quantitatively examines the effects of slope height and slope gradient on landslide size distributions. We developed a loess slide inventory by using field survey data. Statistical analysis shows that most landslides are concentrated in areas with slope heights less than 60 m, and approximately 30% of the loess slides occurred on slopes with a gradient between 30° and 40°. However, high and steep slopes are rare in nature. We calculated the relative density of landslides, and the results showed that the relative density of landslides is greater on higher slopes with steeper slope gradients. Moreover, landslide size is correlated with slope height and slope gradient. The results demonstrate that landslide size increases as slope height increases and decreases as slope gradient increases. Furthermore, we determined the probability density of landslide area using kernel density estimation. The results showed that the landslide size distribution exhibits power law scaling above a certain size threshold, and the size threshold differs for different slope heights and slope gradients. The exponential scalings are influenced by slope height and slope gradient. Our results indicate that the exponential scaling decreases with increasing slope height and increases with increasing slope gradient. Large landslides are more frequent with a higher slope height and gentler slope gradient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".