Drifting away: implications of changes in ice conditions for a pack-ice-breeding phocid, the harp seal (<i>Pagophilus groenlandicus</i>)
Bibliographic record
Abstract
Harp seals ( Pagophilus groenlandicus (Erxleben, 1777)) required drifting pack-ice for birth, nursing, and as a resting platform for neonates after weaning. Data on the yearly location of whelping patches in the Gulf of St. Lawrence collected between 1977 and 2011 were combined with ice cover data (thickness and duration) to examine whether female harp seals actively select particular ice features as a breeding platform and to describe how these ice features have varied over the last 40 years at three spatial scales: the entire Gulf of St. Lawrence, the southern gulf, and the “traditional whelping area” within the southern Gulf. From our analyses, harp seals prefer the thickest ice stages available in the Gulf: grey–white and first-year ice. Lower than normal ice coverage years were more frequent for the required grey–white and first-year ice than for the total ice cover and less frequent at the “traditional whelping area” scale close to the northwestern coast of the Magdalen Islands than at the Gulf of St. Lawrence scale. The frequency of light ice years increased and the duration of the ice season decreased throughout the last decade. Our study showed that the temporal availability and the spatial distribution of the suitable ice are important when evaluating the effect of changes in ice conditions rather than overall ice extent.
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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 | 0.000 |
| 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".