Viability of the southern elephant seal population of the Falkland Islands
Bibliographic record
Abstract
Abstract Accurate long‐term series of demographic data are available for most populations of southern elephant seals. However, research on the elephant seals of the Falkland Islands began only recently, and information for an accurate forecasting of the future of this population is lacking. In this paper we present data on the current status of the population and its trend in size during the last 11 years. We built an age‐structured model of the population and analyzed the effect of variation in demographic parameters on population growth. Elasticity analysis demonstrated that variation in mortality has a more pronounced effect on instantaneous growth rate than an equivalent variation in fecundity. We examined the effects of environmental variability, inbreeding and catastrophes on population viability by a computer simulation using the VORTEX PVA programme. In the stochastic model, the most important factor affecting extinction risk was variability in mortality rates, in particular of the adult classes. We concluded that, although the population does not appear to be at immediate risk of extinction, its small size and isolation make it necessary to accurately monitor the population trends and to acquire additional information on life history and feeding strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".