Effects of winter surface aeration on pelagic zooplankton communities in a small boreal foothills lake of Alberta, Canada
Bibliographic record
Abstract
Winter aeration is often used to prevent fish winter-kill among lakes supporting recreational fisheries and is often used in conjunction with trout stocking as trout are sensitive to hypoxia.We collected limnological data as well as pelagic microcrustacea and rotifers one year before and one year after aeration was initiated in Birch Lake, located in the boreal foothills ecozone of Alberta, Canada. Using a Before-After-Control-Impact design, we compared changes in Birch Lake to those in two nearby and similarly mesotrophic, stocked control lakes (one aerated for more than six years, one unaerated). During winter, surface aeration increased the depth of well-mixed water in Birch Lake, maintaining levels of dissolved oxygen suitable for supporting stocked trout. Linear mixed model analyses indicated that among May–August water quality parameters, only phosphorus concentrations showed a marginally significant treatment × year interaction, increasing only in the unaerated control lake. Aeration did not affect the spring–summer abundance, biomass, or sizes of Birch Lake's zooplankton community. Once aerated, the microcrustacean community in Birch Lake showed partial overlap with the community in the aerated control, although this could not be statistically linked to changes in specific taxa. Ours is the first study to investigate impacts of winter surface aeration on zooplankton communities despite the numerous lakes managed under this 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".