Factors Affecting Elevated Arsenic and Methyl Mercury Concentrations in Small Shield Lakes Surrounding Gold Mines near the Yellowknife, NT, (Canada) Region
Bibliographic record
Abstract
Gold mines in the Yellowknife, NT, region--in particular, the Giant Mine--operated from 1949-99, releasing 237,000 tonnes of waste arsenic trioxide (As2O3) dust, among other compounds, from gold ore extraction and roasting processes. For the first time, we show the geospatial distribution of roaster-derived emissions of several chemical species beyond the mine property on otherwise undisturbed taiga shield lakes within a 25 km radius of the mine, 11 years after its closing. Additionally, we demonstrate that underlying bedrock is not a significant source for the elevated concentrations in overlying surface waters. Aquatic arsenic (As) concentrations are well above guidelines for drinking water (10 μg/L) and protection for aquatic life (5 μg/L), ranging up to 136 μg/L in lakes within 4 km from the mine, to 2.0 μg/L in lakes 24 km away. High conversion ratios of methyl mercury were shown in lakes near the roaster stack as well, with MeHg concentrations reaching 44% of total mercury. The risk of elevated exposures by these metals is significant, as many lakes used for recreation and fishing near the City of Yellowknife are within this radius of elevated As and methyl Hg concentrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".