A Review of Water Level Fluctuations on Aquatic Biota With an Emphasis on Fishes in Ice‐Covered Lakes<sup>1</sup>
Bibliographic record
Abstract
Abstract: The effects of water level fluctuations on fish and other aquatic biota, with an emphasis on winter water withdrawal in northern regions is reviewed. Water demands for population growth and development are adding pressure on water reserves, particularly when coupled with changing climatic conditions. Water level fluctuations can have adverse effects on the environment, most notably to hydrologic and biotic processes ranging in magnitude from the micro‐scale to landscape level. Water level management of lakes and reservoirs can affect all forms of aquatic biota. The severity of effect is dependant on the magnitude, duration and timing of the fluctuation, and the species exposed. In northwestern Canada and northern Alaska, water is withdrawn from water bodies to construct ice‐roads and other winter based developments. Biota in small, isolated water bodies are particularly sensitive to reductions in winter water levels. Water withdrawals can reduce the oxygen available to overwintering fish, while reduced water levels can reduce habitat for fish and furbearers, and freeze littoral areas killing plants, invertebrates, and fish eggs. Regulatory winter water withdrawal thresholds have been developed in the Northwest Territories and Alaska and continue to be refined as new data becomes available. The use of thresholds can help minimize or avoid negative impacts to the environment, particularly fish, from winter water withdrawal activities. Many different factors may influence the effect that winter water withdrawal has on a water body, such as basin shape, substrate and location. More research is warranted to better understand the linkages between anthropogenic and natural water level fluctuations and their combined effect on aquatic ecosystems. A general decision support system is proposed for minimizing risk to aquatic life from winter water withdrawal activities.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".