The impact of dietary supplementation with astaxanthin on egg quality and growth of long snout seahorse (<i>Hippocampus guttulatus</i>) juveniles
Bibliographic record
Abstract
This study investigated the effect of dietary astaxanthin supplementation on egg quality and juvenile growth of long snout seahorse (Hippocampus guttulatus). Captive breed seahorse broodstock were fed four diets composed of frozen shrimp [Atlantic ditch shrimp, Palaemonetes varians) used as a vector to deliver artificial diets with increasing levels of astaxanthin (0, 75, 100 and 125 mg kg−1 dry weight)]. The results indicated that the astaxanthin uptake into eggs from the enriched shrimp diets was highly efficient. Females fed unsupplemented astaxanthin diet produced similar-sized eggs with lower concentration of astaxanthin than females fed diets with astaxanthin. The lower concentration of astaxanthin in the eggs was correlated with the production of smaller juveniles in comparison with the juveniles hatched from parents fed supplemented astaxanthin diets. Juvenile growth and survival was limited by their size on release from the male's pouch as at the end of 28-day postparturition juveniles produced with the diet with no astaxanthin were still significantly smaller (P < 0.05) than those produced from parents fed astaxanthin-supplemented diets. These results demonstrate a significant benefit of dietary astaxanthin supplementation in long snout seahorse diets in terms of improved egg quality and juvenile growth and survival.
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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".