Digestive efficiency and dry-matter digestibility in Steller sea lions fed herring, pollock, squid, and salmon
Bibliographic record
Abstract
Dry-matter digestibility and energy digestive efficiency were measured in six juvenile Steller sea lions (Eumetopias jubatus) fed three diets each consisting of a single species: herring, pollock, and squid. Two of the animals were also fed pink salmon. Dry-matter digestibility (DMD) and digestive efficiency (DE) were measured using the energy and manganese concentration in fecal and food samples. DE values were high for all prey species (herring: 95.4 ± 0.7% (mean ± SD), pollock: 93.9 ± 1.4%, salmon: 93.4 ± 0.5%, squid: 90.4 ± 1.3%). Steller sea lions appear to digest prey of high energy density more efficiently than prey of low energy density. DMD values were also high for all prey species (herring: 90.1 ± 1.8%, pollock: 86.5 ± 3.4%, salmon: 87.3% ± 2.6, squid: 90.5 ± 1.2%). The low DMD value for pollock compared with herring and squid was due to the high proportion of bony material in pollock. There was a strong linear relationship between DE and DMD for each prey type, but the terms cannot be used interchangeably. DE measures are more meaningful than DMD in conveying the energetic benefits derived by sea lions from different types of prey. Species-specific measures of the digestible energy obtained from an array of prey items are a necessary component in understanding the bioenergetic consequences of consuming different prey species.
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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 | 0.001 | 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".