A framework for the use of conservation hatcheries to support wild Pacific salmon recovery in Canada
Bibliographic record
Abstract
Hatcheries have long been used to produce Pacific salmon ( Oncorhynchus spp.), and in recent years, they have been used to assist the recovery of populations that have declined and become imperiled. For populations where the conservation of biodiversity is a primary goal, we used guidance from Canada's Wild Salmon Policy, and recent scientific advice, to develop a framework for the use of hatchery supplementation in the recovery of wild salmon in Canada to manage the tradeoff between the increase in abundance that a hatchery program can provide, with the risks to wild salmon from supplementation. We use a simple deterministic model to show that hatchery supplementation can play a role in boosting abundance during the early phases of recovery of wild salmon populations, but if natural production does not increase, the population may become dominated by hatchery-origin spawners and may be contrary to biodiversity goals. We conclude that in certain circumstances conservation hatchery programs can be an appropriate tool for the recovery of wild salmon populations, but uncertainty about long-term risks to wild populations requires a cautious approach. Careful planning and ongoing monitoring and program adjustment are needed to ensure that adverse impacts are minimized.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".