Bibliographic record
Abstract
Soil erosion is a major environmental concern in Nepal’s mid-hill regions, particularly in areas like Kaski District where steep slopes, intense rainfall, and changing land use contribute to land degradation. This study aims to assess soil erosion loss in Kaski District by applying the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) models. Utilizing available datasets such as SRTM DEM for slope and aspects data, CHIRPS rainfall data, NARC soil data and ICIMOD land use landcover map, soil erosion loss maps were generated to identify soil erosion patterns. The results show that both USLE and RUSLE models effectively captured the spatial variability of soil erosion across the district, with USLE producing slightly higher estimates than RUSLE. Erosion rates were found to be lower in the southern parts of the district and increased progressively toward the northern mountainous regions. The model outputs were consistent with findings from previous studies in Kaski and similar terrain, supporting the validity of these models for erosion prediction in Kaski. The study highlights the usefulness of USLE and RUSLE as practical tools for soil loss estimation and provides a foundation for future research, land management planning, and soil conservation initiatives in the region.
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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 | 0.000 |
| 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 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".