MétaCan
Menu
Back to cohort
Record W4385541410 · doi:10.1016/j.aqrep.2023.101667

Feed supplementation with winery by-products improves the physiological status of juvenile Liza aurata during a short-term feeding trial and hypoxic challenge

2023· article· en· W4385541410 on OpenAlexfundno aff
Francisca P. Martínez-Antequera, Luis Molina-Roque, Verónica de las Heras, Juan Miguel Mancera, Juan António Martos-Sitcha, Francisco Javier Moyano

Bibliographic record

VenueAquaculture Reports · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
FundersUniversidad de CádizJunta de AndalucíaCanadian Academy of Sport and Exercise Medicine
KeywordsBiologyFood sciencePomaceJuvenileWineLeesAquacultureFeed conversion ratioFish <Actinopterygii>FisheryEcology

Abstract

fetched live from OpenAlex

The search of bioactive compounds obtained from natural sources with beneficial effects in growth and health is an increasing trend in aquaculture. Wine by-products are an excellent source of such compounds, mostly phenolics, with demonstrated antioxidant and immunostimulant activities in vertebrates. The present study evaluated the effects of dietary inclusion (100 g/kg) of two wine by-products (grape pomace and lees) on growth, immune status and metabolism of juvenile golden gray mullet (Liza aurata), as well as the potential protective effect of compounds present in the two by products against induced stress produced by moderate hypoxia. Results evidenced a significant positive effect of grape pomace on feed efficiency, as well as in different indicators of metabolic and immunological status of the fish. Also, a significant negative effect of wine lees on the functional diversity of intestinal microbiota was evidenced. Fish fed on diets containing any of the two by-products evidenced significantly lower levels of cortisol when challenged by hypoxia, this pointing to a protective effect mediated by their contents in phenolic compounds and suggesting an interesting and practical application for these agricultural by-products.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.250
Teacher spread0.222 · 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 designBench or experimental
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

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAquaculture ReportsSame topicAquaculture Nutrition and GrowthFrench-language works237,207