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Record W3081747033 · doi:10.1111/anu.13091

Using stable isotope analysis to assess the relationship among dietary protein sources, growth, nutrient turnover and incorporation in Nile tilapia ( <i>Oreochromis niloticus</i> )

2020· article· en· W3081747033 on OpenAlexaff
Hui Zhou, Binhe Gu

Bibliographic record

VenueAquaculture Nutrition · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsMinistry of Agriculture
FundersGuangdong Science and Technology Department
KeywordsNile tilapiaOreochromisBiologyProtein turnoverAnimal scienceδ13CStable isotope ratioFish mealSoybean mealNutrientδ15NMealNitrogenTilapiaBiochemistryFood scienceFish <Actinopterygii>EcologyFisheryChemistryProtein biosynthesis

Abstract

fetched live from OpenAlex

Nile tilapia (1.20 g) were fed for 56 days in five treatments (FM100 containing only fish meal [FM] as a protein source; soybean meal (SBM) replace 25%, 50%, 75% and 100% of FM protein in the diets of FM75, FM50, FM25 and FM0, respectively) to assess the effects of dietary protein sources on growth, turnover half-life of nitrogen and carbon stable isotopes (δ13C and δ15N) in muscle, isotope discrimination factors (Δ15N and Δ13C), and the relative contribution of protein sources to muscle growth. Results showed that the final body weight, weight gain and specific growth rate of fish fed diet FM25 were significantly higher than other treatments (p < .05). By day 35, fish under all treatments reached isotopic equilibrium, and the equilibrium values consistently increase as a function of stable isotope values in diets. Growth accounted for most isotopic changes in muscle and resulted in significantly shorter half-lives of nitrogen and carbon in FM25 (p < .05). The Δ15N of FM25 and Δ13C of FM0 were significantly lower than other treatments (p < .05). The proportional contributions of SBM protein were significantly higher than their respective levels in FM75 and FM25 (p < .05), indicating a preferential incorporation of nitrogen from the SBM in muscle tissue.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.252
Teacher spread0.211 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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