Algal and Vegetable Oils as Sustainable Fish Oil Substitutes in Rainbow Trout Diets: An Approach to Reduce Contaminant Exposure
Bibliographic record
Abstract
The aim of this study was to replace 75% of total fish oil (FO) and alter digestible protein/digestible energy (DP/DE) in rainbow trout feeds to minimize potentially hazardous environmental contaminant exposure. Two diets differing in DP/DE ratios (18 and 25) were combined with soybean (SO), canola (CO), and a blend of canola oil and Schizochytrium sp. (COS). Dietary lipids and DP/DE ratios did not affect apparent digestibility, growth, and somatic parameters. The n-3/n-6 levels decreased significantly in the growth trial, especially for the SO groups. A short washout trial restored n-3/n-6 levels for the CO and COS groups, irrespective of the DP/DE ratio, but not for the SO groups. At all sampling events, contaminant concentrations in fish flesh were lower than limits set by regulatory agencies in Canada, the US, and Europe. Contaminants were lower in the oil replacement diets compared to FO for toxaphenes, organochlorine pesticides, and PCBs but not flame retardants during the growth phase. At the end of the washout phase, no differences were detected. Thus, this study revealed that replacing 75% of total fish oil in rainbow trout feed by CO and COS, combined with a 25 DP/DE ratio, with a washout period seems to be the most efficient approach in terms of maximizing the total FO replacement and contribute to reducing POPs exposure.
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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".