Net energy gained by northern fur seals (<i>Callorhinus ursinus</i>) is impacted more by diet quality than by diet diversity
Bibliographic record
Abstract
Understanding whether northern fur seals (Callorhinus ursinus (L., 1758)) are negatively affected by changes in prey quality or diversity could provide insights into their on-going population decline in the central Bering Sea. We investigated how six captive female fur seals assimilated energy from eight different diets consisting of four prey species (walleye pollock (Gadus chalcogrammus Pallas, 1814, formerly Theragra chalcogrammus (Pallas, 1814)), Pacific herring (Clupea pallasii Valenciennes in Cuvier and Valenciennes, 1847), capelin (Mallotus villosus (Müller, 1776)), and magister armhook squid (Berryteuthis magister (Berry, 1913))) fed alone or in combination. Net energy was quantified by measuring fecal energy loss, urinary energy loss, and heat increment of feeding. Digestible energy (95.9%–96.7%) was high (reflecting low fecal energy loss) and was negatively affected by ingested mass and dietary protein content. Urinary energy loss (9.3%–26.7%) increased significantly for high-protein diets. Heat increment of feeding (4.3%–12.4%) was significantly lower for high-lipid diets. Overall, net energy gain (57.9%–83.0%) was affected by lipid content and varied significantly across diets. Mixed-species diets did not provide any energetic benefit over single-species diets. Our study demonstrates that diet quality was more important in terms of energy gain than diet diversity. These findings suggest that fur seals consuming low-quality prey in the Bering Sea would be more challenged to obtain sufficient energy to satisfy energetic and metabolic demands, independent of high prey abundance.
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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".