Heavy Metal Pollution in the Receiving Environment of the University of Dar Es Salaam Waste Stabilization Ponds
Bibliographic record
Abstract
The aim of this study was to examine the availability, concentration levels and bioaccumulation of heavy metals namely; Mercury (Hg), Cadmium (Cd), Lead (Pb), Zinc (Zn) and Molybdenum (Mo) in the waste Stabilization Ponds of the University of Dar es Salaam. A total of 135 samples were analyzed, out of which 27 were samples of water, 27 of sediments and 81 samples of fish tissues. Two types of fishes were used namely; Oreochromis niloticus and Clarias gariepinus. Heavy metal concentration varied significantly between water, sediment, fish species and tissues. Hg, Cd, Zn and Mo concentrations in water and sediment were within WHO safe limits. However, Pb in water and Cd, Pb and Zn concentrations in sediments were found to be above WHO standards (p<0.05). Concentration levels for Cd, Pb and Zn were above acceptable levels in Oreochromis niloticus while Hg was found to be within safe limits in both fish species (p<0.05). Molybdenum was found to be below the detection limits in Oreochromis niloticus. While Cd was not detected, Pb, Zn and Mo were found highly accumulated in Clarias gariepinus (p<0.05). Oreochromis niloticus accumulated metals in the increasing order from dorsal muscles < gills < liver while Clarias gariepinus accumulated metals in the decreasing order from dorsal muscle < gills < liver.Public awareness on the dangers to which fish consumers from the site are exposed is highly suggested and purposeful mitigation measures of stopping all fishing activities in these sites is needed, also animal feeding around the ponds should be forbidden.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".