Influence of Supplemental Dietary Cholesterol on Growth Performance, Indices of Stress, Fillet Pigmentation, and Upper Thermal Tolerance of Female Triploid Atlantic Salmon (Salmo salar)
Bibliographic record
Abstract
The salmon aquaculture industry must be proactive at developing mitigation tools/strategies to offset the potential negative impacts of climate change. Therefore, this study examined if additional dietary cholesterol could enhance salmon production at elevated temperatures. We hypothesized that supplemental cholesterol could aid in maintaining cell rigidity, reducing stress and the need to mobilize astaxanthin muscle stores, and improving salmon growth and survival at high rearing temperatures. Accordingly, postsmolt female triploid salmon were exposed to an incremental temperature challenge (+0.2°C day-1) to mimic conditions that they experience in sea cages in the summer, with temperature held at both 16 and 18°C for several weeks [i.e., 3 weeks at 16°C, followed by an increase at 0.2°C day-1 to 18°C (10 days), then 5 weeks at 18°C] to prolong their exposure to elevated temperatures. From 16°C onwards, the fish were fed either a control diet, or one of two nutritionally equivalent experimental diets containing supplemental cholesterol [+1.30%, experimental diet #1 (ED1); or +1.76%, experimental diet #2 (ED2)]. Adding cholesterol to the diet did not affect the salmon’s incremental thermal maximum (ITMax), growth, plasma cortisol, or liver stress-related transcript expression. However, ED2 appeared to have a small negative impact on survival, and both ED1 and ED2 reduced fillet “bleaching” above 18°C as measured using SalmoFan™ scores. Although the current results suggest that supplementing salmon diets with cholesterol would have few/minimal benefits for the industry, ≤ 5% of the female triploid Atlantic salmon used in this study irrespective of diet died before temperature reached 22°C. These latter data suggest that it is possible to produce all female populations of reproductively sterile salmon that can withstand summer temperatures in Atlantic Canada.
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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".