The effects of prey availability on pup mortality and the timing of birth of South American sea lions (<i>Otaria flavescens</i>) in Peru
Bibliographic record
Abstract
Abstract Pup mortality and the timing of birth of South American sea lions Otaria flavescens were investigated to determine the possible relationship between fluctuations in prey availability in the Peruvian upwelling ecosystem and current and future reproductive success of sea lions during six consecutive breeding seasons. Our study from 1997 to 2002 encompassed the strongest El Niño on record and one La Niña event. Pup mortality ranged from 13% before El Niño to 100% during El Niño, and was negatively correlated with prey availability. Abortions were also more frequent when prey availability was low. However, pup mortality remained high following El Niño due to the punctuated short‐term effects it had on population dynamics and subsequent maternal behaviour. Births occurred later in the season after years of low food availability and earlier following years of high food availability. The peak of pupping occurred around the peak of mortality in all years, and may have been the product of intensive competition between bulls at the peak of the breeding season. The stronger and more frequent El Niños that appear to be occurring along the Peruvian coast may produce significant stochastic changes in future births and pup mortality, which may place the vulnerable South American sea lion population in Peru at greater risk.
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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".