Effects of aeration on water quality in agricultural reservoirs in the northern Great Plains
Bibliographic record
Abstract
Lerminiaux J, Norton B, Wilson RJ, Rimas R, Lavender TM, Finlay K. 2024. Effects of aeration on water quality in agricultural reservoirs in the northern Great Plains. Lake Reserv Manage. XX:XXX–XX.Aeration of agricultural reservoirs is an encouraged practice in the northern Great Plains of Canada as it can improve water quality by reducing pathogenic bacteria and algal abundance. Cattle also prefer aerated water, resulting in greater weight gain. Despite its known benefits, agricultural reservoir aeration is still not uniformly adopted, largely given its cost of installation and maintenance. Wind powered aeration has been shown to be an eco-friendly and sustainable way to increase oxygen levels in agricultural reservoirs, but the mechanisms by which this aeration can improve water quality are not well documented. By comparing 5 aerated agricultural reservoirs to 5 unaerated agricultural reservoirs, we evaluated whether wind-powered aeration improves water quality. We measured dissolved oxygen, nitrogen, and phosphorus, algal biomass (as chlorophyll a), cyanobacteria abundance, and algal toxins (microcystin, anatoxin-a, and β-N-methylamino-L-alanine) in the aerated and unaerated agricultural reservoirs on a weekly basis over a 12-week period from mid June through August 2022. We found that the aerators were able to mix the water column and add oxygen to deeper regions of the agricultural reservoir, but this did not result in consistent improvements to any measured water quality parameter. The observed lack of a strong response to aeration suggests that this practice may provide only minimal water quality benefits, but aeration may still prove beneficial for deep (>2 m) and sheltered agricultural reservoirs that do not regularly mix from wind effects alone.
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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 | 0.000 | 0.000 |
| 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 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".