Changes in Tissue Lipid and Fatty Acid Composition of Farmed Rainbow Trout in Response to Dietary Camelina Oil as a Replacement of Fish Oil
Bibliographic record
Abstract
Camelina oil (CO) replaced 50 and 100 % of fish oil (FO) in diets for farmed rainbow trout (initial weight 44 ± 3 g fish(-1)). The oilseed is particularly unique due to its high lipid content (40 %) and high amount of 18:3n-3 (α-linolenic acid, ALA) (30 %). Replacing 100 % of fish oil with camelina oil did not negatively affect growth of rainbow trout after a 12-week feeding trial (FO = 168 ± 32 g fish(-1); CO = 184 ± 35 g fish(-1)). Lipid and fatty acid profiles of muscle, viscera and skin were significantly affected by the addition of CO after 12 weeks of feeding. However, final 22:6n-3 [docosahexaenoic acid (DHA)] and 20:5n-3 [eicosapentaenoic acid (EPA)] amounts (563 mg) in a 75 g fillet (1 serving) were enough to satisfy daily DHA and EPA requirements (250 mg) set by the World Health Organization. Other health benefits include lower SFA and higher MUFA in filets fed CO versus FO. Compound-specific stable isotope analysis (CSIA) confirmed that the δ(13)C isotopic signature of DHA in CO fed trout shifted significantly compared to DHA in FO fed trout. The shift in DHA δ(13)C indicates mixing of a terrestrial isotopic signature compared to the isotopic signature of DHA in fish oil-fed tissue. These results suggest that ~27 % of DHA was synthesized from the terrestrial and isotopically lighter ALA in the CO diet rather than incorporation of DHA from fish meal in the CO diet. This was the first study to use CSIA in a feeding experiment to demonstrate synthesis of DHA in fish.
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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 | 0.001 | 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".