Examining Seasonality of Zooplankton Communities and Water Quality in Yellowknife Area Lakes, Northwest Territories
Bibliographic record
Abstract
Frame Lake, located in Yellowknife, Northwest Territories, is one of the city's main attractions. Unfortunately, urbanization and eutrophication have led to a decline in water quality in the lake. Eutrophication has caused a buildup of organic matter in sediments on the bottom of the lake, which can cause a high oxygen demand when processed by aerobic bacteria. Dissolved oxygen levels are high during the summer, but ice shields the lake from gas exchange with the atmosphere, leading to winter anoxia. This oxygen depletion makes it impossible for sensitive aquatic life, such as fish, to survive in the lake over the winter. One possible solution to winter anoxia is to install a hypolimnetic aerator to exchange oxygen between the lake's deep layers and the atmosphere. Past studies show that aeration will improve dissolved oxygen levels, and it may help break down accumulated organic matter and reduce nutrient levels in the water column. As part of a remediation project funded by Rio-Tinto Diamond Mines, a hypolimnetic aerator will be installed on Frame Lake in early summer 2024. To evaluate the effects of the aerator installation on the lake, data on the natural variability in water quality and biota in Frame Lake and comparable lakes within the region is necessary.\nThe objectives for my thesis were to examine seasonal variability in water quality and zooplankton communities in Frame Lake and two reference lakes, and to examine the relationships between water quality and zooplankton communities in those lakes to assess if aeration might lead to chemical or biological improvements in Frame Lake. I collected monthly water quality and zooplankton samples from Frame Lake and two reference lakes between March 2022 – November 2024. My results showed seasonal fluctuations in pH, conductivity, and dissolved oxygen in my study lakes, and confirmed that Frame Lake experiences anoxia once the ice cover forms. In addition, nutrient and conductivity levels were higher in Frame Lake than in the two reference lakes. Zooplankton samples showed seasonal variation in richness, diversity, evenness, and abundance, with highs in the summer and lows under ice in the winter. In addition, the abundance of zooplankton in Frame Lake was significantly lower under ice in comparison with the two reference lakes. When examining correlations between zooplankton communities and water quality variables that may be influenced by aeration, I found that zooplankton abundance and richness increased with higher levels of dissolved oxygen and decreased with higher levels of total nitrogen. These findings suggest that the installation of a hypolimnetic aerator in Frame Lake is needed to improve oxygen levels in Frame Lake during the winter, and that it may improve conditions such that zooplankton abundance and richness increase. Ultimately, the chemical and biological improvements that may result from aeration could improve the suitability of Frame Lake for future fish reintroduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".