Effect of dietary coriander oil and vegetable oil sources on fillet fatty acid composition of rainbow trout
Bibliographic record
Abstract
Randall, K. M., Reaney, M. J. T. and Drew, M. D. 2013. Effect of dietary coriander oil and vegetable oil sources on fillet fatty acid composition of rainbow trout. Can. J. Anim. Sci. 93: 345–352. A 16-wk feeding trial was conducted to examine the effect of adding coriander oil to vegetable oil (VO) diets on the bioconversion of linoleic acid (LA; 18:2n-6) to arachidonic acid (ARA; 20:4n-6) and alpha-linolenic acid (ALA; 18:3n-3) to eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3) in rainbow trout. The experimental treatments were a 4×2 factorial arrangement of diets using four dietary oils (fish, flax, canola and camelina oils) and two levels of coriander oil (0 and 5 g kg−1 inclusion levels). Twenty-four tanks of triploid female rainbow trout (130 g initial weight; n=3) were used in the experiment. The experiment lasted 112 d during which fish were fed to satiation twice per day. The fatty acid composition of fillets from coriander-fed fish had increased concentrations of 20:5n-3 and 22:6n-3 (P<0.05). Furthermore, a trend to increased (20:5n-3+22:6n-3)/20:4n-6 ratios was seen when coriander oil was added to the diet (P=0.067). These results suggest that the addition of coriander oil to VO diets can significantly increase the bioconversion of 18:3n-3 to 20:5n-3 and 22:6n-3 in rainbow trout.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".