Seasonal Turbidity Linked to Physical Dynamics in a Deep Lake Following the Catastrophic 2014 Mount Polley Mine Tailings Spill
Bibliographic record
Abstract
Abstract The catastrophic August 2014 Mount Polley tailings spill, the second largest ever documented, sent ~18 Mm 3 of waste plunging to the bottom of the >100 m deep West Basin of Quesnel Lake, British Columbia, a critical West Coast salmon habitat. To understand the impact of the spill on the lake, including the fate of suspended solids, we examine changes in physical water properties over 11 years (2006–2017) using water column profiles, moored timeseries, and satellite imagery. Contaminated waters were initially largely confined to the hypolimnion; however, during autumn 2014 turnover, turbid waters were mixed to the surface, resulting in the clear blue lake turning bright green. Twelve months after the spill, the lake's temperature, conductivity, and turbidity temporarily returned to pre‐spill conditions; however, initiation of mine effluent discharge in late 2015 was associated with a subsequent 15 μS cm −1 conductivity increase above historic values. Importantly, a post‐spill 1–2.5 formazin turbidity unit hypolimnetic turbidity increase was observed during spring and fall turnovers of 2015–2017, which appeared to be due to resuspension of a thin layer of unconsolidated spill‐related material from the lake bed driven by large internal seiche motions. This process implies spill contaminants may be seasonally mobilized into the water column, with potentially detrimental impacts on aquatic ecology. Our findings underscore that basin‐scale physical processes, including seasonal turnover and internal seiches, must be accounted for, even in deep lakes, to understand the long‐term impact of the ever increasing number of tailings spills into aquatic ecosystems.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".