The Effects of Climate Change on Harp Seals (Pagophilus groenlandicus)
Bibliographic record
Abstract
Harp seals (Pagophilus groenlandicus) have evolved life history strategies to exploit seasonal sea ice as a breeding platform. As such, individuals are prepared to deal with fluctuations in the quantity and quality of ice in their breeding areas. It remains unclear, however, how shifts in climate may affect seal populations. The present study assesses the effects of climate change on harp seals through three linked analyses. First, we tested the effects of short-term climate variability on young-of-the year harp seal mortality using a linear regression of sea ice cover in the Gulf of St. Lawrence against stranding rates of dead harp seals in the region during 1992 to 2010. A similar regression of stranding rates and North Atlantic Oscillation (NAO) index values was also conducted. These analyses revealed negative correlations between both ice cover and NAO conditions and seal mortality, indicating that lighter ice cover and lower NAO values result in higher mortality. A retrospective cross-correlation analysis of NAO conditions and sea ice cover from 1978 to 2011 revealed that NAO-related changes in sea ice may have contributed to the depletion of seals on the east coast of Canada during 1950 to 1972, and to their recovery during 1973 to 2000. This historical retrospective also reveals opposite links between neonatal mortality in harp seals in the Northeast Atlantic and NAO phase. Finally, an assessment of the long-term trends in sea ice cover in the breeding regions of harp seals across the entire North Atlantic during 1979 through 2011 using multiple linear regression models and mixed effects linear regression models revealed that sea ice cover in all harp seal breeding regions has been declining by as much as 6 percent per decade over the time series of available satellite data.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".