Nutritional evaluation of seal by-products as an alternative protein source for use in monogastric animals
Bibliographic record
Abstract
Seal by-products (grey seal with the bone in, grey seal with the bone removed, and harp seal) were subjected to five different processing methods: high (100 °C) and low (45 °C) temperature oven-drying, freeze-drying (FD), silage by acid or natural fermentation. Growth performance of diets containing these seal by-products was evaluated in rats as a monogastric model species. With the exception of naturally fermented grey seal without bone, weight gains for rats fed the boneless grey seal products were highest of the seal products (24.65–30.04 g rat−1) and statistically similar to those of rats fed casein (32.15 g rat−1). An in vivo crude protein (CP) digestibility study was conducted using 12 adult white rats in metabolic cages that allowed separate collection of urine and feces. The 16% CP diets contained chromic oxide as an inert fecal marker at 0.5%. Total fecal and urine production, as well as feed and water intake, were recorded daily within the three experimental periods. Digestibility of CP was significantly higher for the naturally fermented grey seal without bone silage (94.0%) than casein (89.0%), whereas the other seal products were statistically similar (91.7%–92.7%). The CP content of FD grey seal was as high as 91.7% (FD). Seal by-products have the potential to be used as alternative high-protein feedstuffs in monogastric diets.
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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) | 0.001 | 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 | 0.001 |
| 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".