Prey composition impacts lipid and protein digestibility in northern fur seals (<i>Callorhinus</i> <i>ursinus</i>)
Bibliographic record
Abstract
Pinnipeds have specific macronutrient (protein, lipid) requirements to satisfy physiological functions, yet little is known about how diet characteristics affect macronutrient digestibility. We measured relative and absolute lipid and protein digestibility in six female northern fur seals (Callorhinus ursinus (Linnaeus, 1758)) fed eight experimental diets composed variously of four prey species (Pacific herring, Clupea pallasii Valenciennes in Cuvier and Valenciennes, 1847; walleye pollock, Gadus chalcogrammus Pallas, 1814 (formerly Theragra chalcogramma (Pallas, 1814)); capelin, Mallotus villosus (Müller, 1776); magister armhook squid, Berryteuthis magister (Berry, 1913)). We quantified how digestibility was affected by proximate composition of the diet (% lipid or % protein), levels of food mass and macronutrient intake, and tested for any potential benefit of multi-species diets. Overall, digestibility of both protein and lipid were high across diets, although macronutrient retention of lipids (96.0%–98.4%) was significantly higher than protein (95.7%–96.7%) for all but the two highest protein diets. Increased levels of protein intake resulted in increased protein retention, but decreased lipid digestibility. There was no evidence that mixed-species diets provide greater macronutrient digestibility over single-species diets. The results suggest that high to moderate lipid diets are more beneficial to northern fur seals because they lead to increased levels of lipid retention without large decreases in protein digestibility. This raises concerns that dietary factors may be contributing to the population declines of northern fur seals in the Bering Sea.
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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 | 0.001 |
| 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".