Optimizing long chain-polyunsaturated fatty acid synthesis in salmonids by balancing dietary inputs
Bibliographic record
Abstract
The increasing use of terrestrial plant lipids to replace of fish oil in commercial aquafeeds requires understanding synthesis and storage of long chain-polyunsaturated fatty acids (LC-PUFA) in farmed fish. Manipulation of dietary fatty acids may maximize tissue storage of LC-PUFA, through increased production and selective utilization. A data synthesis study was conducted to estimate optimal levels of fatty acids that may maximize the production and storage of LC-PUFA in the edible portion of salmonids. Data were compiled from four studies with Atlantic salmon, rainbow trout, and steelhead trout (total n = 180) which were fed diets containing different terrestrial-based oils to replace fish oil. LC-PUFA (%) were linearly correlated between diet and muscle tissue (p < 0.001; r2 > 44%), indicating proportional storage after consumption. The slope, or retention rate, was highest for docosahexaenoic acid (DHA) at 1.23, indicating that an additional 23% of DHA was stored in the muscle. Dietary saturated fatty acids were positively related to DHA stored in the muscle (p < 0.001; r2 = 22%), which may involve membrane structural requirements, as well as selective catabolism. DHA was found to be optimally stored with a dietary n-3: n-6 ratio of 1.03: 1. These new results provide a baseline of optimal dietary ratios that can be tested experimentally to determine the efficacy of balancing dietary fatty acids for maximum LC-PUFA storage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".