A Synthesis of Surface Water Quality in Awash Basin, Ethiopia
Bibliographic record
Abstract
Developing countries like Ethiopia are grabbling with rapid population growth, urbanization, agricultural intensification, and climate change which put intense pressure on the availability and quality of water resources. The surface water quality degradation is exacerbating due to increasing urbanization and agricultural activities. The average annual fertilizer use in Ethiopia increased from 132,522 metric tons (mt) in 1996 to 858,825 mt in 2015. Pesticide use also increases significantly from 3,327.7 mt/y in 2006 to 4,211.5 mt/y in 2010. The Awash river is one of the most affected rivers by intensified irrigation schemes, industrial, and urbanization pollution. The Awash river and its tributaries are used for domestic, irrigation, industrial, and recreational purposes. However, as per Canadian water quality indices for the drinking and irrigation water quality, the upper Awash basin scored 34.79, and 46.39, respectively, in the poor and marginal categories; whereas the middle/lower basin indicated 32.25 and 62.78 in poor and marginal ranges, respectively. Dissolved phosphorous in the headwater tributaries is about 0.51 mg/l which is beyond the threshold (0.15 mg/l). The surface water quality impairment is severe in the upper Awash basin where more than 90% of Addis Ababa's industries discharge their waste into nearby waterways without treatment; about 30% of the population lacks access to a liquid waste disposal and treatment facility; only 16% of the population is connected to sewage system, and 25% of the total waste generated enters freshwater systems without treatment. Many studies on surface water quality are reviewed and many of them are inconclusive for a number of reasons. For example, no comprehensive surface water quality research, lack of detailed combined spatial and temporal surface water quality data, and analysis to show the overall picture of the basin are a few of them. Despite the existence of the policy and legal tools, enforcement is lacking. Improving the ecological health of rivers necessitates policy revision as well as increased knowledge and engagement among implementers.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".