A re-examination of the relationship between Steller sea lion (<i>Eumetopias jubatus</i>) diet and population trend using data from the Aleutian Islands
Bibliographic record
Abstract
Prey diversity and energy density have been linked to each other and to population trends in many studies of bird and mammal diets. We re-examined these relationships in Steller sea lions (Eumetopias jubatus (Schreber, 1776)) using data collected from the Aleutian Islands, where there has been a strong longitudinal gradient in population trend. Diet diversity and energy density metrics were similar in the western Aleutians, where sea lion counts declined consistently, and in the easternmost Aleutian area, where population trends improved significantly. We compared traditional deterministic diet diversity metrics with diversity scores based on an occupancy model that accounts for differences in sample size and uncertainty in prey group detection. This analysis indicated that there was no significant change in diet diversity over the 23-year study period or any significant differences across the Aleutian Islands. These results are consistent with prey abundance data from nine groundfish bottom trawl surveys conducted over the same period. While diet studies detail what Steller sea lions eat and provide an estimate of their energy intake, they provide only limited information on the energy expended to obtain their food or the consequences of their diet and foraging ecology on individual or population fitness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".