Survival of <i>Escherichia coli</i> O157:H7 in ruminal or fecal contents incubated with corn or wheat dried distillers’ grains with solubles
Bibliographic record
Abstract
Dried distillers' grain with solubles (DDGS) is a by-product of ethanol production, and its use as cattle feed has increased as a result of the expansion of the fuel ethanol industry. However, the inclusion of corn DDGS into feedlot diets may increase the shedding of Escherichia coli O157:H7. This study investigated whether corn or wheat DDGS at 2 concentrations (20% or 40% vs. 100% barley grain) affected the survival of E. coli O157:H7 in incubations of ruminal digesta and feces. Neither the type nor the level of DDGS had any effect on fermentation or the survival of E. coli O157:H7 in ruminal digesta. However, there was a time by DDGS interaction (p < 0.05), where the numbers of E. coli O157:H7 in feces did not differ after 4 or 12 h of incubation but were greater after 24 h in both 40% wheat and 40% corn DDGS as compared with other treatments. Additionally, after 24 h, the numbers of E. coli O157:H7 were greater in fecal incubations with corn DDGS than with wheat DDGS (p < 0.05). The differences in the numbers of E. coli O157:H7 were not attributable to changes in pH or in concentrations of volatile fatty acids in the media. These results suggest that the inclusion of high levels of corn or wheat DDGS in feedlot diets of cattle may encourage the survival of E. coli O157:H7 in feces.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".