Adapting management of Pacific salmon to a warming and more crowded ocean
Bibliographic record
Abstract
Abstract The North Pacific Ocean is warming and overall Pacific salmon abundance is higher now than at any other time in the past century. This increase in abundance is in large part due to warming-related changes in marine ecosystems at northern latitudes that primarily benefit pink salmon, and industrial-scale hatchery production to support commercial fisheries. A large body of evidence indicates that increasing and more variable ocean temperatures, as well as competition among salmon at sea, are associated with shifts in salmon productivity, body size, and age at maturation. However, these relationships vary by species, location, and time, resulting in increased harvest opportunities in some regions and exacerbated conservation concerns in others. The weight-of-evidence suggests North Pacific salmon nations should, as a minimum, limit further increases in hatchery salmon production until there is a better scientific understanding of hatchery and wild salmon distribution at sea, how they interact, and how the consequences of these interactions are influenced by broader climate and ecosystem conditions. Coordinated research to overcome knowledge gaps and develop strategies to reduce unintended interactions between hatchery and wild salmon could be funded (in part) by a tax placed on industrial-scale hatchery salmon releases. A tax would formalize recognition that there are finite prey resources to support salmon in the ocean and that both prey and wild salmon represent a “common property” whose use should not be without cost to those that seek to benefit from them. We highlight additional approaches salmon nations can take to adapt to changing conditions and suggest that improved communication and collaboration among North Pacific salmon research and management agencies will be key to balancing the benefits and risks of a warming and more crowded ocean.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".