Total Mercury and Methylmercury in Lake Water of Canada’s Oil Sands Region
Bibliographic record
Abstract
Abstract Increased delivery of mercury to ecosystems is a common consequence of industrialization, including in the Athabasca Oil Sands Region (AOSR) of Canada. Atmospheric mercury deposition has been studied previously in the AOSR; however, less is known about the impact of regional industry on toxic methylmercury (MeHg) concentrations in lake ecosystems. We measured total mercury (THg) and MeHg concentrations for five years from 50 lakes throughout the AOSR. Mean lake water concentrations of THg (0.4–5.3 ng L–1) and MeHg (0.01–0.34 ng L–1) were similar to those of other boreal lakes and <5% of all samples exceeded Provincial water quality guidelines. Lakes with the highest THg concentrations were found >100 km northwest of oil sands mines and received runoff from geological formations high in metals concentrations. MeHg concentrations were highest in those lakes, and in smaller productive lakes closer to oil sands mines. Simulated annual average direct deposition of THg to sampled lakes using an atmospheric chemical transport model showed <2% of all mercury deposited to sampled lakes was emitted from oil sands activities. Consequently, spatial patterns of mercury in AOSR lakes were likely most influenced by watershed and lake conditions, though mercury concentrations in these lakes may be perturbed with future development and climatic change.
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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 | 0.002 | 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) | 0.001 | 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".