Using stable isotope analysis to assess the relationship among dietary protein sources, growth, nutrient turnover and incorporation in Nile tilapia ( <i>Oreochromis niloticus</i> )
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".