A Systematic Review of Heavy Metals of Anthropogenic Origin in Environmental Media and Biota in the Context of Gold Mining in Ghana
Bibliographic record
Abstract
Heavy metal accumulation in the food chain is an issue of global concern because it eventually leads to toxic effects on humans through the water we drink, contaminated soils, crops, and animals. Reports of toxicant levels in environmental media (air, water, and soil) and biota in Ghana were sought in SCOPUS, PubMed, MEDLINE, and EMBASE. Of 1004 bibliographic records identified, 54 studies were included in evidence synthesis. A disproportionately large number of papers (about 80%) focused exclusively on environmental media. Papers focusing on biomonitoring and human health were relatively few. Studies reported a high degree of spatial variability for the concentrations of 8 metals in groundwater. Generally, heavy metal concentrations in soil reported by the studies reviewed were higher than metal concentrations in riverine sediments. Urine and hair were the most common biological markers of heavy metal exposure used by the studies reviewed unlike nails, which were sparingly used. By and large, published results on the levels of heavy metals in goldmine and non-mine workers yielded contradictory results. Mostly, concentrations of heavy metals reported by the studies reviewed for nails were higher than for hair. A high degree of variability in the heavy metal concentrations in human subjects in the studies reviewed is likely due to heterogeneity in physiological states, excretion profiles, and body burdens of individuals. These, in turn, may be a product of genetic polymorphisms influencing detoxification efficiency.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".