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