Can ice breeding seals adapt to habitat loss in a time of climate change?
Bibliographic record
Abstract
Climate change associated declines in sea ice will have serious impact on species that rely on ice for reproduction and/or feeding. Little is known about the impacts on ice-dependent, sub-Arctic species or on how these species may adapt, although the ecosystem changes are likely to be most rapid along the ice edge. Harp seals (Pagophilus groenlandicus) require stable ice for pupping, nursing and the first weeks after weaning when the young develop the capacity to swim and feed. Although ice conditions in the Northwest Atlantic have varied over the past 40 years, in 2010 and 2011, the total extent of ice suitable for whelping harp seals was at, or near, the lowest ever recorded. These years of exceptionally poor ice provided us with an opportunity to improve our understanding about how ice breeding seals may respond to the conditions expected in the future. Harp seals responded to poor ice conditions differently, depending on the presence or absence of ice at the beginning of the pupping period. If no ice was present, females moved away from their traditional whelping areas to find suitable ice. If small amounts of ice were present, females gave birth even if the ice was too thin to sustain the pups, resulting in high pup mortality. There was no evidence to indicate that harp seals pupped on land even in areas where ice was absent. Young seals that drifted to shore had high levels of abandonment and mortality. If the predicted warming trends continue, ice-breeding harp seals will encounter more years with poor ice conditions and may eventually adapt by moving north. Until then, they will continue to have increased levels of mortality that could result in the disappearance of the most southern breeding component in the Gulf of St Lawrence.
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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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".