Assessment of microbiome changes after rumen transfaunation: implications on improving feed efficiency in beef cattle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Understanding the host impact on its symbiotic microbiota is important in redirecting the rumen microbiota and thus improving animal performance. The current study aimed to understand how rumen microbiota were altered and re-established after being emptied and receiving content from donor, thus to understand the impact of such process on rumen microbial fermentation and to explore the microbial phylotypes with higher manipulation potentials. RESULTS: Individual animal had strong effect on the re-establishment of the bacterial community according to the observed profiles detected by both fingerprinting and pyrosequencing. Most of the bacterial profile recovery patterns and extents at genus level varied among steers; and each identified bacterial genus responded to transfaunation differently within each host. Coriobacteriaceae, Coprococcus, and Lactobacillus were found to be the most responsive and tunable genera by exchanging rumen content. Besides, the association of 18 bacterial phylotypes with host fermentation parameters suggest that these phylotypes should also be considered as the regulating targets in improving host feed efficiency. In addition, the archaeal community had different re-establishment patterns for each host as determined by fingerprint profiling: it was altered after receiving non-native microbiome in some animals, while it resumed its original status after the adaptation period in the other ones. CONCLUSIONS: The highly individualized microbial re-establishment process suggested the importance of considering host genetics, microbial functional genomics, and host fermentation/performance assessment when developing effective and selective microbial manipulation methods for improving animal feed efficiency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it