Salmon shark connections: North Pacific climate change, indirect fisheries effects, or just variability?
Bibliographic record
Abstract
Abstract A variety of changes are occurring in the ecosystems of the North Pacific Ocean and Bering Sea, but information about the mechanisms of change has been relatively limited, due in part to the region’s remoteness and subarctic conditions. Any number of ecosystem components or indicators could be used to exemplify this dilemma, but here we point to the salmon shark (Lamna ditropis, Lamnidae) as an example of a species that can potentially mediate considerable ecosystem change due to its high trophic level, but for which some basic information is lacking despite attracting some interesting research and widespread rumours and anecdotal evidence of increased abundance. Increases in the abundance of sharks such as salmon sharks in this region during the 1990s, if true, may help explain other observed changes such as declines in ocean survival rates of Pacific salmon (Oncorhynchus spp., Salmonidae) in the region and declines in some wild salmon stocks. Mechanisms that could cause salmon shark increases in Alaskan coastal waters include: (i) increases in sea temperature since the 1970s allowing distributional shifts of this species and changes in the abundance or distribution of their prey; (ii) the 1992 banning of high seas drift gillnets; and (iii) indirect fisheries effects such as competitive release of salmon sharks in the North Pacific transition region and towards the more southern geographic extent of their annual migration as the result of fishery‐related reductions in blue sharks (Prionace glauca, Carcharhinidae) and other pelagic predators. The relative plausibility of these alternative explanations can be evaluated using combinations of existing ecosystem models and empirical research and monitoring programmes including local and indigenous observations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".