Transgenic camelina oil is an effective source of eicosapentaenoic acid and docosahexaenoic acid in diets for farmed rainbow trout, in terms of growth, tissue fatty acid content, and fillet sensory properties
Bibliographic record
Abstract
Abstract The oilseed Camelina sativa was genetically engineered to produce eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) at levels similar to fish oil (FO), which we tested as a lipid source in diets for rainbow trout. Three experimental diets were tested, a FO control, a low‐level transgenic camelina oil (LCO) diet (no FO with 12.5 g/kg camelina), and a high‐level transgenic camelina oil (HCO) diet (no FO with 130 g/kg transgenic camelina). Trout (initial weight 49.8 ± 11 g/fish) were fed for 12 weeks and were evaluated for growth performance, fatty acid content (muscle, liver, brain, and eye), and sensory properties of fillets. The final lengths and weights of fish fed LCO and HCO diets were higher compared to fish fed FO. There were no differences in weight gain, condition factor, specific growth rate, feed intake, and feed conversion ratio. EPA in muscle tissue was higher in fish fed HCO and FO diets compared to the LCO diet. Compound‐specific stable isotope analysis revealed that rainbow trout fed the FO diet stored isotopically enriched DHA compared to the lighter DHA in fish fed HCO. Trout fillets from the HCO treatment were firmer in texture and had a higher orange intensity compared to the FO group.
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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 | 0.000 | 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".