Heavy Metals in Sediments of Gilgel Gibe I Hydroelectric Dam Reservoir and its Tributaries
Bibliographic record
Abstract
Aquatic ecosystems are susceptible to pollution with heavy metals arising from anthropogenic and natural sources. In this study the pH, organic matter content, and concentrations of selected heavy metals (Cd, Pb, Cu, Cr, Co) in the sediments of Gilgel Gibe I Hydroelectric Reservoir and its tributaries were investigated. Sediment samples were taken by grab sampling from 5 sample sites. Microwave digestion was used for the extraction of the samples prior to quantitative determination of the target heavy metals by Flame Atomic Absorption Spectroscopy (FAAS).The obtained concentrations of the heavy metals were varied from 0.80 ± 0.46 - 10.4 ± 0.68 mg/kg (Cd), 0.80 ± 0.46 - 10.4 ± 0.68 mg/kg (Pb), 17.26 ± 1.94 - 28.38 ± 0.30 mg/kg ( Cr);0.55 ± 1.38 - 9.74 ± 0.45 mg/kg (for Cu); and 7.53±0.51 - 18.50±1.11 g/kg (Co). One-way ANOVA (p < 0.05) indicated that the concentrations the studied heavy metals were significantly different among the sediment samples collected from the dam reservoir and its tributaries. The dam reservoir sediment accumulate higher concentration of Cd (i.e., 10.4±0.68 mg/kg) and this value is above the sever effect level of National Oceanic and Atmospheric Administration (NOAA) and Canadian interim sediment quality guideline. Nada Kala contribute highest amount of metal accumulation compared with other tributaries. The degree of metal pollution was analyzed in terms of contamination factor and Cd has showed moderate contamination of reservoir sediment. Generally, the obtained findings demonstrated that the studied sediments have high concentrations of target heavy metals. Speciation study is recommended to determine the desorption tendency of the heavy metals towards water column as well as their risk on aquatic ecosystems.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".