Effects of feeding grains naturally contaminated with Fusarium mycotoxins with and without a polymeric glucomannan mycotoxin adsorbent on reproductive performance and serum chemistry of pregnant gilts1
Bibliographic record
Abstract
Contamination of animal feedstuffs with Fusarium mycotoxins can cause reduced feed intake and hyperaminoacidemia resulting from reduced hepatic protein synthesis. The current study investigated the effects of feeding grains naturally contaminated with Fusarium mycotoxins on reproductive performance, serum chemistry, ADFI, and ADG of gilts, and tested the ability of a polymeric glucomannan mycotoxin adsorbent (GMA) to reduce or eliminate the effects of the contaminated feeds. Thirty-six Yorkshire gilts were fed 3 diets (n = 12 gilts/diet) from 91 +/- 3 d of gestation until farrowing. Diets included 1) control, 2) contaminated grains, and 3) contaminated grains + 0.2% GMA. Diets contaminated with Fusarium mycotoxins did not affect ADFI (P = 0.24), but ADG (P = 0.029) and G:F (P = 0.047) were reduced. Serum concentrations of beta-hydroxybutyrate, haptoglobin, protein, albumin, globulin, urea, glucose, cholesterol, Ca, Na, Mg, P, K, and Cl, and hepatic enzyme activities were not affected by diet. The frequency of stillborn piglets was greater (P = 0.03) for gilts fed contaminated grains compared with that of gilts fed contaminated grains + GMA. The feeding of contaminated grains + GMA also increased (P = 0.026) the percentage of pigs born alive compared with gilts fed the contaminated diets. In conclusion, feeding gilts diets that are naturally contaminated with Fusarium mycotoxins can increase the incidence of stillborn piglets and this effect can be reduced by dietary supplementation with GMA.
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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".