High deoxynivalenol and ergot alkaloid levels in wheat grain: effects on growth performance, carcass traits, rumen fermentation, and blood parameters of feedlot cattle
Bibliographic record
Abstract
This study was designed to assess the impacts of a mixture of deoxynivalenol (DON) and ergot alkaloids (EAs) on growth performance, rumen function, blood parameters, and carcass traits of feedlot cattle. Forty steers (450 ± 6.0 kg) were stratified by weight and randomly allocated to 1 of 4 treatments; control-low (CON-L), control-high (CON-H) which contained low or high wheat screenings that lacked mycotoxins at the same level as the mycotoxin-low (MYC-L; 5.0 mg/kg DON, 2.1 mg/kg EA), and mycotoxin-high (MYC-H: 10 mg/kg DON, 4.2 mg/kg EA) diets that included wheat screening with mycotoxins. Steers were housed in individual pens for a 112-day finishing trial. Intake was 24.8% lower (P < 0.001) for MYC steers compared to CON steers. As a result, average daily gains of MYC steers were 42.1% lower (P < 0.001) than CON steers. Gain to feed ratio was also lower (P < 0.001) for MYC steers compared to CON steers. Platelets, alanine aminotransferase, globulins, and blood urea nitrogen were lower (P ≤ 0.008), and lymphocytes, glutathione peroxidase activity (GPx), and interleukin-10 (IL-10) were elevated (P ≤ 0.002) in MYC steers compared to CON steers. Hot carcass weights and backfat thickness were reduced (P < 0.001) in MYC steers, resulting in leaner (P < 0.001) carcasses and higher (P < 0.007) meat yield compared to CON steers. Results suggest that a mixture of DON and EAs negatively impacted health, performance, and carcass traits of feedlot steers, with the majority of this response likely attributable to EAs. However, more research is needed to distinguish the relative contribution of each mycotoxin to the specific responses observed.
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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) | 0.001 | 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".