Physico-Chemical Water Quality Assessment of Gilgel Abay River in the Lake Tana Basin, Ethiopia
Bibliographic record
Abstract
The physico-chemical parameters such as PH, temperature, electrical conductivity, total dissolved solid, turbidity, total alkalinity, total hardness, ammonia, nitrate, nitrite, phosphate, sulphate, sulfide and iron were investigated to assess the various water quality parameters along the River course of Gilgel Abay River (GAR).The value of those parameters have been evaluated with respect to guidelines provided by World Health Organization(WHO), Ethiopian drinking water quality standards(EDWQS), Canadian Council of Minister for Environment(CCME) and European Community(EC) to indicate the pollution level of GAR.Overall compliance was 58.93%.From a total of 224 samples, 132 samples (58.93%) complied with WHO guidelines and EDWQS.Turbidity, followed by iron, phosphate and sulfide were the prime river water quality issues identified in GAR.Analysis of variance was used to examine the variations of water quality parameters between the dry and rainy seasons, and the variations along the river courses of Gilgel Abay (upper, middle and lower course).The majority of the parameters showed that there is a significant variation of the water quality parameters between the dry and rainy seasons.However, the variations along the river courses of GAR (upper, middle and lower course) were statistically insignificant.This implies that the GAR water quality is influenced by anthropogenic impacts from the upper course to the lower course of the River.
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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 | 0.003 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".