Foraging behavior of adult female Steller sea lions during the breeding season in Southeast Alaska
Bibliographic record
Abstract
Abstract During the 1990s, the Steller sea lion (Eumetopias jubatusSchreber) Western Alaska stock (WS) suffered steep population decline while the Eastern Alaska stock (ES) steadily increased. One bottom‐up forcing hypothesis explaining this decline predicted lactating adult female foraging behavior would be different between stocks. To investigate this effect, we monitored 11 ES females at two breeding rookeries using satellite dive recorders (SDR) during the early breeding seasons of 1992–1993, examined their behavior with respect to prey, physiological limitations, and habitat, and made limited comparisons to observations of WS female behavior reported in the literature. ES females were not operating at the extremes of ability, with most diving within the limits of aerobic metabolism, less than one‐quarter of possible foraging time during trips spent submerged and most foraging trips requiring less than one‐half the lipid store fasting ability of dependent pups. Thus, females may have some capacity to alter behavior to accommodate future changes in foraging conditions, but the extent of this plasticity is unknown. Because recent work suggests WS recovery is impeded by low natality, future studies should test differences between reproductive and non‐reproductive mature females in order to properly assess the contribution of foraging ecology to SSL population dynamics.
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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 | 0.000 | 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".