Assessment of Levels, Speciation, and Toxicity of Trace Metal Contaminants in Selected Shallow Groundwater Sources, Surface Runoff, Wastewater, and Surface Water from Designated Streams in Lake Victoria Basin, Uganda
Bibliographic record
Abstract
The levels, speciation of elements, and toxicity of selected trace metals as well as other parameters in selected surface water, shallow groundwater sources, landfill leachate, and associated surface runoff in the Lake Victoria basin, Uganda, were studied. The WHO guidelines, Ugandan standards, Canadian guidelines and Swedish EPA were used for assessment. The shallow groundwater was acidic with pH values below 6.5. The pH, dissolved organic carbon, flouride, and sulphate levels for all springs were below the guideline values although 52.8% was contaminated with nitrates while 39% was contaminated with chloride ions. Some surface water samples had levels of major elements, such as iron, chromium, aluminium, and manganese, above the guideline values. Speciation studies showed that 74% of the metal ions was bound to dissolved organic matter in surface water, whereas in landfill leachates, the dominant ionic species was metal hydroxides or fulvic acid bound. Risk analysis based on the Swedish EPA showed varied risks of negative effects in 30%–76% of the sample sites ranging from high to increased risk in surface water, whereas the results from modelling sorption data using the Bio-met tool showed potential risk to toxicity effects of Cu 2+ , Ni 2+ , Zn 2+ , and Pb 2+ in 15.3%–30.8% surface water samples and 8.3%–62.5% groundwater samples.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".