Causes of offspring mortality in the Antarctic fur seal, <i>Arctocephalus gazella</i>: the interaction of density dependence and ecosystem variability
Bibliographic record
Abstract
Rates of pup production and causes of pup mortality, recorded in a designated study colony on Bird Island, South Georgia, from 1989 to 2003, were used to evaluate the factors influencing the growth of the population of Antarctic fur seals, Arctocephalus gazella (Peters, 1875). The mean number of pups produced per year was 680 (range 444–822) with a mean survival rate of 77.6% (range 52.6%–92.8%). Starvation, arising from reduced food availability within the mothers' foraging area, was the most frequently recorded cause of death and was positively correlated with the overall rate of pup survival, although it showed no relationship with the number of pups produced. However, traumatic injury showed a local relationship with seal density, increasing significantly with increasing numbers of seals born. This suggests that environmental processes that reduce the availability of prey to lactating mothers, rather than space limitation within colonies, are the limiting factor in the population increase of Antarctic fur seals at South Georgia. The spatial scales over which such processes operate, relative to the local-scale effects of densities of animals within colonies, have important implications for the future expansion of the population and the resultant trophodynamic interactions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".