A Synthesis of Surface Water Quality in Awash Basin, Ethiopia
Why this work is in the frame
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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.
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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) | 0.005 | 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 it