Brown seaweed (AquaArom) supplementation increases food intake and improves growth, antioxidant status and resistance to temperature stress in Atlantic salmon, Salmo salar
Bibliographic record
Abstract
Seaweeds represent a vast resource that remains underutilized as an ingredient in aquafeeds. Here we probed the effect of addition of AquaArom, a seaweed meal derived from brown seaweeds (Laminaria sp., kelp), to fish feed on growth performance, antioxidant capacity and temperature responsiveness of mitochondrial respiration. A commercial salmonid feed was mixed with 0 (control), 3, 6 and 10% seaweed and fed to Atlantic salmon (Salmo salar) smolts for 30 days. The smolts consumed more of the seaweed-supplemented food relative to the control and there were no mortalities. Compared with the control, the final fish weight, standard length, weight gain and SGR were higher in fish fed diets supplemented with the 3 and 10% seaweed, while growth performance for fish maintained on 6% seaweed remained neutral. Importantly, seaweed supplementation increased protein efficiency ratio (PER) and tended to improve food conversion ratio (FCR). Although the hepatosomatic and visceral indices did not change, whole gut and intestinal weights and lengths were higher in fish maintained on seaweed-supplemented diets suggesting increased retention time and a larger surface area for food digestion and nutrient absorption. Measurement of antioxidant status revealed that seaweed supplementation dose-dependently increased plasma total antioxidant capacity as well as the level of glutathione, and activities of catalase and superoxide dismutase in liver mitochondria. Moreover, seaweed supplementation reduced the effect of acute temperature rise on mitochondrial respiration and proton leak. Overall, these data suggest that AquaArom can be mixed with fish food up to 10% to increase food consumption and enhance growth performance, as well as to improve antioxidant capacity and alleviate adverse effects of stressors such as temperature 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 | 0.000 | 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".