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Record W3048966667 · doi:10.20873/uftv7-8141

GARLIC POWDER IN RATIONS FOR NILE TILAPIA

2020· article· en· W3048966667 on OpenAlexaboutno aff
Elton Lima Santos, Maria do Carmo Mohaupt Marques Lüdke, José Milton Barbosa, Emerson Carlos Soares, Misleni Ricarte de Lima, Jorge Vitor Lüdke, Carlos Bôa-Viagem Rabello

Bibliographic record

VenueDESAFIOS Revista Interdisciplinar da Universidade Federal do Tocantins · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsNile tilapiaPelletsTilapiaFecesAnimal scienceCompletely randomized designPelletFood scienceBiologyNutrientChemistryFish <Actinopterygii>OreochromisFisheryEcology

Abstract

fetched live from OpenAlex

The This study evaluated the effect of levels of garlic powder (0.0, 1.0, 2.0 and 3.0 g.kg-1) added to diets for Nile tilapia on animal performance, villi height and diet digestibility. The rations consisted of pellets and tilapia and were fed to apparent satiation for 60 days. 120 fish, with initial average weight of 4.20 ± 0.4 g, were masculinized and distributed in 24 aquariums of 70L in a randomized design consisting of four treatments and six replications in a closed circulation system with constant aeration. 240 fishes was used for feces collected by an indirect method (Guelph modified system) and estimation of coefficients of apparent digestibility using 0.5 g.kg-1 of chromic oxide as an inert indicator incorporated into the ration. No significant differences in performance related to organ weight, the hepato-somatic index, the viscero-somatic fat index and villi height were observed. However, the apparent digestibility coefficient of rations was negatively affected by the addition of garlic powder. Garlic powder as an additive in rations for Nile tilapia did not provide positive effects as a growth promoter and worsened the digestibility of nutrients among the tested treatments.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score1.000

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)0.0010.000

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 head0.253
Teacher spread0.224 · 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.

Study designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueDESAFIOS Revista Interdisciplinar da Universidade Federal do TocantinsSame topicAquaculture Nutrition and GrowthFrench-language works237,207