Biogeochemistry of U, Ni, and As in two meromictic pit lakes at the Cluff Lake uranium mine, northern Saskatchewan
Bibliographic record
Abstract
Open pits, which remain after uranium (U) mining operations cease, can form meromictic lakes that develop suitable conditions for the containment of dissolved and colloidal metals. In this study, the distribution and speciation of U, nickel (Ni), and arsenic (As) in the water column of two meromictic pit lakes was investigated at the decommissioned Cluff Lake mine in northern Saskatchewan. The 28 m deep and older D pit had a chemocline at a 13 m depth, below which it turned anoxic and its meromixis was controlled by iron (Fe) cycling. Below the chemocline both Fe(III) and As(V) were reduced to Fe(II) and As(III), respectively. Iron cycling had a large effect on U distribution because reducing conditions prevented sulfide oxidation and a drop in pH in deeper layers. Metal-reducing bacteria were found to be present at, and below, the chemocline. In the deeper (90 m), larger and more recently flooded DJX pit, two chemoclines were observed at depths of 15 and 65 m. Both were linked to sharp U and Ni concentration gradients. Unlike the D pit, a transition to reducing conditions was not observed in the DJX pit’s water column. However, colloidal U, primarily associated with aluminum oxyhydroxides, was found below the first chemocline. Overall, the meromixis type determined the distribution and speciation of metals and bacteria in the investigated pit lakes, thus providing insights into the use of pit lakes as a potential bioremediation strategy.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".