Age-specific growth, survival, and population dynamics of female Australian fur seals
Bibliographic record
Abstract
Postsealing population recovery rates of fur seals and sea lions have differed markedly, perhaps owing to habitat type. Australian fur seals ( Arctocephalus pusillus doriferus Wood Jones, 1925) employ a benthic foraging mode similar to sea lions, and have exhibited similarly slow population recovery. Nonetheless, the population doubled in recent decades, suggesting a recent change in demographic rates. In the present study, the frequency and size of known-age females (n = 297) were used to create body growth and survivorship models. These were compared with models obtained in the 1970s before the recent population increase. Body growth, which is relatively rapid in comparison to other fur seal species, remains unchanged since the 1970s, suggesting that density-dependent effects are absent despite the population increases. Adult survival rates (weighted mean: 0.885) have increased greatly since the 1970s and are the likely mechanism of the recent increases. Total population abundance was estimated to be 4.5 times that of pups. Australian fur seals display high survivorship, rapid body growth, low fecundity, and low population growth rates; all are characteristics typical of benthic foraging sea lions rather than other fur seals.
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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 | 0.000 | 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".