Improving efficacy of landscape interventions in the (sub) humid Ethiopian highlands by improved understanding of runoff processes
Bibliographic record
Abstract
Despite millions of dollars invested in soil and water conservation practices in the (sub) humid Ethiopian highlands and billions of hours of food-for-work farm labor, sediment concentration in rivers is increasing. This paper reports on the research to reverse the current trend. Based on the understanding of the hydrology of highlands, we provide evidence on sources of surface runoff and sediment and on mechanisms that govern the erosion processes and approaches and how they affect soil and water conservation practices. We suggest that priority in landscape interventions should be given to re-vegetation of the degraded areas so as to reduce the sediment concentration contributions originating from these areas. Additionally, efforts should be directed to gully rehabilitation in the saturated bottom landscape that may consist of vegetating shallow gullies and stabilizing head cuts of deeper gullies Finally, rehabilitation efforts should be directed to increase the rain water infiltration in the upland areas through the hard pan layer by connecting the land surface to the original deep flow paths that exist below about 60 cm. It will reduce the direct runoff during the rainy season and increase baseflow during the dry season.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".