Weather, climate, and entry into migration of northern fur seal pups
Bibliographic record
Abstract
In the migration of young animals, environmental cues can play an outsized role in dispersal, ontogeny, and potentially survival. Identifying and quantifying such cues promotes an understanding of individual species’ migratory evolution and response to long-term environmental change. This study examines weather as a proximate factor for initiating first migration in a wide-ranging subpolar marine predator, the northern fur seal (laaqudax̂, in Unangam Tunuu; Callorhinus ursinus (Linnaeus 1758)). Observations of satellite-telemetered pups on three islands in the eastern Bering Sea, Alaska (US) are used to quantify how inclement weather (high winds, snow, and low temperatures) increases departure rate. Historical weather is then used to reconstruct departure, from the mid-20th century onward. Contemporary surveys provide a test for reconstructed estimates and highlight behavioral processes near the entry to migration. Reconstructions provide novel climate context for large-scale population declines in the eastern Bering Sea since the 1950s; within their limitations, there is a lack of evidence for trends in departure or significant influence on demography. Results here build upon and support historical knowledge regarding the role of weather, while also highlighting potential areas of future study – such as maternal behavior – in influencing the entry into first migration in this species.
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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".