Anticipating Soil Erosivity of Kulfo Watershed in the Southern Main Ethiopian Rift in Response to Changes in Land Use and Land Cover
Bibliographic record
Abstract
This study evaluates the land use and land cover (LULC) dynamics that play an indispensable role in the degradation and deterioration of soil and water quality affecting the natural resources throughout the Kulfo watershed in Ethiopia. Directed image classification is initiated for satellite images to study the watershed. The image classification is categorized into ten different LULC classes with validation of ground control points. A Revised Universal Soil Loss Equation (RUSLE) model was used to generate the average soil loss of the watershed. The model involves the Rainfall Erosivity factor (R), Soil Erodibility factor (K), Length and Slope factor (LS), Cover Management factor (C), and Support Practice factor (P). The dynamics of LULC change and rainfall erosivity over the past 30 years have been interpreted using maps from 1990, 2005, and 2020 using the C-factor and R-factor. The remaining factors, like K-factor, LS-factor, and P-factor, were kept constant over the period. The results reveal that the average annual soil loss rate (A) of the watershed is estimated to be 138.8 t ha-1, 161.2 t ha-1, and 173.25 t ha-1 per year, for the selected period intervals. During the past three decades, the soil loss rate in the watershed has increased by 34.4 t ha-1 per year. The watershed and sustainable soil and water conservation practices need special attention to mitigate the severity of soil erosion risks to avoid disaster.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".