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Record W2222467709 · doi:10.1111/anu.12394

The impact of dietary supplementation with astaxanthin on egg quality and growth of long snout seahorse (<i>Hippocampus guttulatus</i>) juveniles

2016· article· en· W2222467709 on OpenAlexaff
Jorge Palma, José Pedro Andrade, Dominique Bureau

Bibliographic record

VenueAquaculture Nutrition · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAstaxanthinBiologySeahorseBroodstockJuvenileFisheryAnimal scienceShrimpSnoutCaptivityZoologyFood scienceEcologyFish <Actinopterygii>AquacultureCarotenoid

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.283
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2016
Admission routes1
Has abstractyes

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