Spatial and temporal dynamics of monsoon-induced landslides in Nepal in 2020
Bibliographic record
Abstract
In the monsoon season, landslides are major disasters in Nepal, causing loss of life and economic impacts. The landslides triggered in the 2020 monsoon (June – September) in Nepal caused more than 300 fatalities and affected about 800 families. A spatial and temporal database of landslides in this region does not exist, which has hindered an understanding of landslide dynamics and the development of a regional early warning system (EWS). In this study, we prepare a time-stamped (hourly) geo-referenced database of the landslides triggered by the 2020 monsoon in Nepal and investigate their dynamic trends. We track landslides from online news for each day during the monsoon to map their location and time. The database contains 332 mapped landslides, out of which accurate time stamps are available for 126 landslides. The spatial pattern shows a large concentration of landslides in central Nepal (districts of Parbat, Kaski, Myagdi, Baglung, Gulmi, and Syangja). The temporal pattern reveals that landslides in this region occur mostly during late night or early morning. We estimate hourly rainfall thresholds for landslide occurrence from the Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall product. The database and analysis provide a basis for estimating regional rainfall thresholds for Nepal and the design of an EWS.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".