Effect of Varying the Concentration of Dietary Astaxanthin on Rainbow Trout,<i>Oncorhynchus mykiss</i>Muscle Pigmentation
Bibliographic record
Abstract
The effect of daily distribution of various dietary astaxanthin concentrations (0, 10, 15, 25, and 30 mg astaxanthin/kg diet) on rainbow trout muscle pigmentation was studied during 9 weeks. Four combined daily distribution treatments (morning/evening) of dietary astaxanthin (25/25, 25/10, 15/15, and 30/0) were tested on triplicate groups of 80 immature rainbow trout (body weight±SD, 199.6±3.7 g). Trout muscle pigmentation was evaluated on the basis of muscle astaxanthin retention and muscle color. Fish muscle astaxanthin increased with increasing dietary carotenoid concentration. There was no significant (P > 0.05) difference for muscle astaxanthin retention coefficients and muscle color parameters of fish receiving treatments 25/25 and 25/ 10 while there was a significant (P < 0.05) difference for muscle astaxanthin retention coefficients and muscle color parameters of fish receiving treatments 25/25 and 15/15 or 30/0. Astaxanthin retention coefficients were higher for lower dietary doses (15/15 or 30/0) while muscle color parameters were lower for fish receiving treatments 15/15 or 30/0. These results suggest that the level of dietary astaxanthin currently used for trout pigmentation (25/25) may be reduced by 30% by varying its daily distribution in feeds (25/10).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".