Reservoir fertilisation and fishery response in a highly managed reservoir with uncertain flows: Ecosystem‐based management using decision analysis
Bibliographic record
Abstract
Abstract Inland fisheries managers must account for multiple competing uses for aquatic resources; using methods such as ecosystem‐based management allows for different priorities for aquatic ecosystems to be accounted for. Declining abundance of kokanee salmon Oncorhynchus nerka (Walbaum) in Arrow Lakes Reservoir in the 1990s led to the use of large‐scale nutrient addition to improve productivity of kokanee and large piscivores. However, it is unclear what effect these measures had on the system given high discharge and highly variable annual flow regime throughout the watershed. An Ecopath with Ecosim model of the ecosystem was fitted to the available data and used to predict ecosystem structure and reservoir objectives under different nutrient addition strategies and varying annual flow regimes. Results from the model indicate that nutrient addition is an important driver in the system, with lower flows resulting in higher biomass for higher trophic levels. Decision analysis demonstrated the importance of maintaining nutrient additions to achieve management objectives despite losses in some high‐flow years.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".