Characterization of the rumen and fecal microbiome in bloated and non-bloated cattle grazing alfalfa pastures and subjected to bloat prevention strategies
Bibliographic record
Abstract
Frothy bloat is an often fatal digestive disorder of cattle grazing alfalfa pastures. The aim of this study was to investigate ruminal and fecal microbiota dynamics associated with development of alfalfa-induced frothy bloat and to further explore how bloat prevention strategies influence the composition of these microbial communities. In a 3 × 3 crossover experiment, twelve rumen-cannulated steers were sequentially subjected to: (1) pure alfalfa pasture, (2) pure alfalfa pasture supplemented with the pluronic detergent ALFASURE, and (3) alfalfa - sainfoin mixed pasture. Eleven out of 12 steers in pure alfalfa pasture developed clinical bloat, whereas ALFASURE treatment prevented the development of bloat in all 12 steers and alfalfa - sainfoin prevented bloat in 5 out of 11 steers. Development of bloat was associated with considerable shifts in the microbiota profile of rumen contents. In particular, the microbiota of solid rumen contents from bloated steers contained higher species richness and diversity. Streptococcus, Succinivibrio and unclassified Myxococcales were enriched in the rumen microbiota of bloated steers, whereas Fibrobacter and Ruminococcus were overrepresented in the rumen contents of non-bloated steers. Our results provide novel insights into bloat-associated shifts in the composition and predicted functional properties of the rumen microbiota of cattle grazing alfalfa pasture.
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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".