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Record W4293227707 · doi:10.1155/2022/1613030

Effects of Menadione Sodium Bisulphite (Vitamin K3) Supplementation of the Diets Based on Plant Feed Ingredients on Growth and Bone Health of Gilthead Seabream (Sparus aurata) Fingerlings

2022· article· en· W4293227707 on OpenAlexaff
David Domínguez, Pedro Ángel Castro Alonso, Santosh P. Lall, Daniel Montero, María Jesús Zamorano, Ramón Fontanillas

Bibliographic record

VenueAquaculture Nutrition · 2022
Typearticle
Languageen
FieldNursing
TopicVitamin K Research Studies
Canadian institutionsNational Research Council Canada
FundersEuropean Commission
KeywordsBiologyAquacultureVitaminFood scienceFeed conversion ratioCommercial fish feedFish mealMenadioneVitamin CAnimal scienceFish <Actinopterygii>FisheryBiochemistryBody weightEndocrinology

Abstract

fetched live from OpenAlex

The nutritional profile of aquaculture feeds can be altered with substitution of fish-based ingredients, including the vitamin contents, ultimately affecting the vitamin supply to fish. Requirements for several vitamins have been described for early life stages of species including some as salmonids and carps, but optimum levels for gilthead seabream fingerlings have not been described for most vitamins. In order to evaluate the essentiality of vitamin K in gilthead seabream fingerlings, a basal diet with low levels of marine ingredients (FM 10% and FO 6%) designed to contain five supplementation levels of vitamin K3 (0.1, 2.8, 6.3, 12.0, and 23.0 mg kg-1) was tested for 70 days. Growth and productive parameters were monitored along the trial and samples for X-ray analyses, and bone and liver molecular markers, histology, and proximal composition were taken. At the end of the experiment, growth, grp expression, and the reduced prevalence of skeletal disorders suggested that dietary vitamin K3 levels for gilthead seabream fingerlings should be above 12 mg kg-1 in diets containing a larger proportion of plant protein and vegetable oils.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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