Accumulating\nMercury and Methylmercury Burdens in\nWatersheds Impacted by Oil Sands Pollution
Bibliographic record
Abstract
Bitumen mining and\nupgrading in northeastern Alberta, Canada, releases\ntoxic pollutants into the atmosphere, including mercury (Hg) and methylmercury\n(MeHg). This Hg and MeHg is then deposited to the surrounding landscape;\nhowever, the fate of these contaminants remains unknown. Here, we\ncompare snowpack chemistry to high-frequency measurements of river\nwater quality across six watersheds (five impacted by oil sands development\nand one unimpacted). Catchment scale snowpack Hg and MeHg loads normalized\nto watershed area were highest near oil sands operations. River water\nHg concentrations and loads tracked discharge and tended to be higher\ndownstream of mining operations, while MeHg concentrations and loads\nincreased through the summer, reflecting peak summer MeHg production\nrates. Except in the reference watershed, snowpack Hg and MeHg loads\nequaled or exceeded the amount of Hg and MeHg exported during freshet\nand, in some cases, the entire hydrologic year. This suggests landscapes\nacross the oil sands region, which are dominated by low-relief wetlands\nand other shallow-water systems, are accumulating Hg and MeHg. Importantly,\nduring years of high discharge, these low-relief systems appear to\nbecome better connected and flush MeHg (and Hg) from the watershed.\nThus, these watersheds may act as temporary, rather than as permanent,\nnatural repositories of oil sands contaminants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.065 | 0.004 |
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; both teacher heads agree on what is shown here.
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".