Effects of Wheat Naturally Contaminated with Fusarium Mycotoxins on Growth Performance and Selected Health Indices of Red Tilapia (Oreochromis niloticus × O. mossambicus)
Bibliographic record
Abstract
An 8-week feeding trial was conducted to examine effects of wheat naturally contaminated with Fusarium mycotoxins (deoxynivalenol, DON 41 mg·kg(-1)) on growth performance and selected health indices of red tilapia (Oreochromis niloticus × O. mossambicus; initial weight = 4.3 g/fish). Five experimental diets were formulated by replacement of clean wheat with naturally contaminated wheat resulting in graded levels of DON and zearalenone (ZEN) (Diet 1 0.07/0.01, Diet 2 0.31/0.09, Diet 3 0.50/0.21, Diet 4 0.92/0.37 and Diet 5 1.15/0.98 mg·kg(-1)). Groups of 50 fish were randomly allocated into each of 20 aquaria and fed to near-satiety for eight weeks. Growth rate, feed intake and feed efficiency of fish fed the experimental diets decreased linearly with increasing levels of Fusarium mycotoxins (p < 0.05). Although growth depression was associated with feeding diets naturally contaminated with Fusarium mycotoxins, especially DON, no biochemical and histopathological parameters measured in blood and liver appeared affected by Fusarium mycotoxin concentrations of diets (p > 0.05). Though there was no clear evidence of overt DON toxicity to red tilapia, it is recommended that feed ingredients should be screened for Fusarium mycotoxin contamination to ensure optimal growth performance.
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 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) | 0.001 | 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".