Receptor modeling, ecological risks, and human health impacts of mercury in some Ghanaian topsoils due to mining and commercial activities
Bibliographic record
Abstract
Globally, elevated environmental mercury levels have been linked to artisanal and small-scale gold mining; however, investigations into mining communities often overlook other potential sources and their contributions to soil mercury accumulation. This study explored the positive matrix factorization (PMF) receptor model to identify other possible sources of mercury contamination in two major mining communities (Kenyasi and Obuasi) and a commercial city (Sunyani) in Ghana. The mercury concentrations across the three study areas showed no significant differences (p = 0.257 at the 95% confidence level). The PMF model identified mining as the major contributor to mercury accumulation in Obuasi and Kenyasi, with other activities, such as farming, also contributing substantially. The generation and burning of hazardous waste in the Sunyani municipality due to increased commercial activities have contributed significantly to mercury contamination. Although the hazard quotient indicated no adverse health effects in the study areas (hazard quotient < 1), the pollution and ecological risk indices showed that Obuasi was significantly enriched with mercury, with considerable levels found in Kenyasi and Sunyani. The results from this study will serve as a good database for environmental studies on mercury particularly in Kenyasi and Sunyani, where there has not been any extensive research on mercury contamination.
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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 | 0.001 |
| 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) | 0.001 | 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".