Review of ecological mercury and arsenic bioaccumulation within historical gold mining districts of Nova Scotia
Bibliographic record
Abstract
Gold mining in Nova Scotia dates back to the mid-1800s. Historical industrial practices generated over 3 000 000 tonnes of finely ground mine wastes (tailings), the majority of which were deposited into lakes, streams, wetlands, and low-lying areas close to the mill sites. These legacy tailings typically contain high concentrations of mercury (Hg) and arsenic (As) and continue to impact downstream environments to this day. The objective of this review is to critically examine and summarize existing knowledge on the transfer and bioaccumulation of Hg and As in aquatic and terrestrial organisms exposed to legacy gold mine tailings in Nova Scotia. This review reveals that as of mid-2019, 23 previous studies have been completed on this subject. Several of these studies were based on small sample sizes, or had other limitations, such as missing identification of biological species. Despite these limitations, the data in these publications clearly indicate that both Hg and As from abandoned gold mine sites in Nova Scotia are bioaccumulating in plants, fungi, freshwater and terrestrial invertebrates, marine mollusks, amphibians, fish, and mammals. In many cases, concentrations of MeHg exceed Canadian Council of Ministers of the Environment safety guideline values for consumption of wildlife; however, no guideline values are in place for either total Hg or As. No studies were found examining tailings-related Hg or As accumulation in lichen, reptiles, birds, or large mammals. This review concludes that further research on bioaccumulation and biomagnification of tailings-related Hg and As is needed to understand the overall impact of historical tailings on the aquatic and terrestrial ecosystems and species of Nova Scotia. More detailed studies are vital for guiding risk-management decisions and future land-use practices for these contaminated sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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 teacher head, 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".