Water resource developments in Ethiopia: potential benefits and negative impacts on the environment, vector-borne diseases, and food security
Bibliographic record
Abstract
To satisfy the growing demand for electricity, Ethiopia plans to increase its electricity production five-fold between 2010 and 2015, mainly through the construction of dams. A literature review shows that while dams can boost power and agricultural production, promote economic development, and facilitate flood control, they can also lead to environmental, ecological, and socioeconomic changes. Several case studies show that dams may alter the composition and density of vectors and intermediate host species, increase the incidence of malaria schistosomiasis and possibly lymphatic filariasis, and lead to eutrophication of reservoirs, soil erosion, and earthquakes. There is evidence that dams and commercial irrigation schemes can increase soil and water degradation, vulnerability to drought, and food insecurity in riverine and lacustrine areas downstream of dams. It appears that dams in Ethiopia are also vulnerable to high soil erosion rates and earthquakes. Consequently, the current and proposed large-scale dam construction program in Ethiopia requires in-depth research to improve our understanding of the unintended negative effects of projects and to guide the location, design, and implementation of appropriate preventive and remedial programs.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".