Effects of dietary vegetable oil supplementation on fillet quality traits, chemical and fatty acid composition of African catfish (<i>Clarias gariepinus</i>)
Bibliographic record
Abstract
Abstract. The effects of dietary fish oil (FO), soybean oil (SO) and linseed oil (LO) (12 % crude fat content each) in African catfish (Clarias gariepinus) diets were tested on the fillet flesh quality, chemical and fatty acid (FA) composition, after 3 and 6 weeks of feeding. The bodyweight gain of fish and the fillet dry matter, crude protein and crude fat content was not different among the divergent treatments. High (>20 %) total n3 FA supplementation significantly increased the moisture loss of fillet (FO, LO). Applying the simple FA dilution model (JOBLING 2004a, 2004b), the incorporation dynamics of the most largely dosed FAs were accurately predictable after 3 weeks (R² between observed and estimated data for total n3 FAs: FO 0.95, LO 0.73 and for α-linolenic acid, LO 0.97). In the fillet FA composition the metabolism of n3 acids was more pronounced. The large provision of α-linolenic acid (LO) had a pronounced effect on the longchain, polyunsaturated n3 FA proportions (eicosapentaenoic and docosapentaenoic acids), while no effect was experienced on docosahexaenoic acid. This study suggests that daily bodyweight gain is not, while fillet flesh quality and FA composition is slightly compromised when fish oil is substituted for vegetable oils.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".