The influence of ground shaking on the distribution and size of coseismic landslides from the Mw 7.6 2005 Kashmir earthquake
Bibliographic record
Abstract
Understanding the conditions that governed the distribution of coseismic landslide frequency and size from past earthquakes is imperative for quantifying the hazard potential of future events. However, it remains a challenge to evaluate the many factors controlling coseismic landsliding including ground shaking, topography, rock strength, and hydrology, among others, for any given earthquake, partly due to the lack of direct seismic observations in high mountain regions. To address the dearth of ground motion observations near triggered landslides, we develop simulated ground motions, including topographic amplification, to investigate these key factors that control the distribution of coseismic landslides from the Mw 7.6 2005 Kashmir earthquake. We show that the combination of strong peak ground motions, steep slopes, proximity to faults and rivers, and lithology control the overall spatial distribution of landslides. We also investigate the role of topographic amplification in triggering the largest landslide induced by this earthquake, the Hattian Bala landslide, finding that it is amplified at the landslide initiation point due to the trapping of energy within the ridge kink as it changes orientation from E to NE. This focusing effect combined with predisposing conditions for hillslope failure may have influenced the location and size of this devastating landslide.
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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 | 0.000 |
| 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) | 0.001 | 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".