Atlantic salmon in a rapidly changing environment—Facing the challenges of reduced marine survival and climate change
Bibliographic record
Abstract
Abstract Atlantic salmon populations have declined in recent decades. Many of the threats to the species during its freshwater and coastal residency periods are known, and management approaches are available to mitigate them. The global scale of climate change and altered ocean ecosystems make these threats more difficult to address. Managers need to be aware that promoting strong, healthy, and resilient wild populations migrating from rivers is the optimal approach currently to reduce the impacts of changing ecosystems and low marine survival. We argue that a fundamental strategy should be to ensure that the highest number of wild smolts in the best condition leave from rivers and coastal areas to the ocean. There is great scope for water quality, river regulation, migration barriers, and physical river habitat improvements. Maintenance of genetic integrity and diversity of wild populations by eliminating interbreeding with escaped farmed salmon, eliminating poorly planned stocking, and reducing impacts that reduce population sizes to dangerously low levels will support the ability of Atlantic salmon to adapt to changing environments. Reducing the impacts from aquaculture and other human activities in coastal areas can greatly increase marine survival in affected areas. As most of the threats to wild salmon are the result of human activities, a focus on human dimensions and improved communication, from scientific and management perspectives, needs to be increasingly emphasized. When political and social will are coupled with adequate resources, managers often have the tools to mitigate many of the threats to wild salmon.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".