Analysis of the fundamental differences between dam-forming landslides and all landslides
Bibliographic record
Abstract
Dam-forming landslides are of significant interest to researchers, as only about 1 % of landslides block rivers, yet these dams can cause extreme flooding when they collapse, with flood flows up to ten times larger than extreme fluvial floods. While regional studies have noted differences in the dimensional characteristics and formation indices between dam-forming and non-dam-forming landslides, a global quantitative comparison has not yet been made. Using open-access global datasets, we conducted a statistical analysis of their morphometric and spatial characteristics, including volume, height/length ratio, and geomorphological factors. Spatial clustering analysis was also performed to determine whether certain landslides are more likely to form dams. The results indicate that dam-forming landslides are a distinct subset: (i) they occur in more upstream areas with higher stream power index values; (ii) they have lower mobility, confined by steeper slopes and shorter hillslope lengths; (iii) shallower landslides with larger surface area and sufficient volume are more likely to form dams; and (iv) they exhibit different spatial clustering patterns compared to general landslides. Despite some data limitations, this global study provides a foundation for quantifying a landslide dam formation index and identifying areas prone to dam formation. • Dam formation landslides is a special subset of all landslides • Dam formation landslides distributed differently compared to all landslides • Dam formation landslides have lower mobility • shallower landslides with larger area and sufficient volume are more likely form dams
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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 | 0.000 | 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 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".