Annual pulses of copper-enriched sediment in a North American river downstream of a large lake following the catastrophic failure of a mine tailings storage facility
Bibliographic record
Abstract
Failures of mine tailings storage facilities (TSF) can have profound and long-lasting effects on the downstream receiving environment. Virtually all spills to date have been into river systems without large lakes that may buffer downstream impacts. In August 2014, the failure of the Mount Polley copper (Cu)-gold mine TSF in British Columbia, Canada, released ~25 × 106 m3 of water and solids; globally, this is the second largest TSF spill in history. Over 18 × 106 m3 was delivered to Quesnel Lake, which is ~9 km from the TSF and is the third deepest lake in North America, and a crucial habitat for Pacific salmon and trout populations. We determined the sediment-associated Cu concentrations and fluxes in Quesnel River, downstream of the lake, from August 2014 to February 2021 based on the analysis of >400 samples of sediment, mainly collected using a continuous-flow centrifuge. During each winter since the spill, Cu concentrations in the fluvial sediment in the upper reaches of the river (~35 km from the TSF) were elevated relative to regional background concentrations and samples collected before the spill. Maximum Cu concentrations were ~410 mg kg−1 which exceeds Canadian sediment quality guidelines for the protection of aquatic organisms (197 mg kg−1). Monitoring of Quesnel Lake since the spill shows that these annual pulses in the winter are due to resuspension of unconsolidated tailings and sediments at the bottom of Quesnel Lake, during autumnal lake turnover, which become mixed throughout the water column and subsequently flow into Quesnel River. Results show that while large lakes may buffer downstream aquatic systems from contaminated sediment, they may prolong the environmental impact. These findings are crucial in understanding how lake processes may modify the effects of TSF spills on downstream aquatic systems.
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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".