Effectiveness of nutrient enhancement as a remediation or compensation strategy of salmonid fisheries in culturally oligotrophic lakes and streams in temperate climates
Bibliographic record
Abstract
Productivity of temperate streams and lakes is often limited by availability of key nutrients, and nutrient‐poor habitats are termed oligotrophic. Oligotrophic streams and lakes occur naturally, but often are a product of human activities (cultural oligotrophication), such as the creation of dams. Cultural oligotrophication has resulted in declining productivity in streams and lakes, a condition that can manifest itself in collapsing salmonid fish stocks. To counteract lost productivity as part of restoration or compensation measures, managers often add nutrient via fertilizers to enhance fisheries production. However, these programs are not always successful, and this article reviews available literature to identify patterns that may influence success of nutrient enhancement programs. Overall fertilization of lakes and streams will almost certainly increase primary producer and invertebrate populations. While it is likely that fertilization will also increase fishery production, it is far from certain. The magnitude of this change is unpredictable, and the success of a fertilization program will vary greatly between years, habitat, and microhabitats. Regardless, if fertilization is coupled with holistic monitoring of the food web and ecosystem, then it is likely to be an effective technique to enhance fishery productivity in active restoration of compensation programs. However, the benefits of fertilization will not outlast the fertilization project, and care must be exercised when ceasing active fertilization. When compared to other restoration/compensation strategies such as fish ladders or trap and transport, fertilization may be a cost‐effective method to enhance fishery production. Finally, recommendations are discussed to increase the probability of fertilization success.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 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".