Effect of Environmental Factors and Influence of Rumen and Hindgut Biogeography on Bacterial Communities in Steers
Bibliographic record
Abstract
Feces from cattle production are considered important sources of bacterial contamination of food and the environment. Little is known about the combined effects of arctic temperatures and fodder tannins on rumen and hindgut bacterial populations. Individual rumen liquor and rectal fecal samples from donor steers fed either alfalfa silage or sainfoin (Onobrychis viciifolia Scop.) silage and water ad libitum were collected weekly on the first three sampling days and fortnightly afterwards. The daily ambient temperatures were registered and averaged to weekly mean temperatures. Steers fed sainfoin silage had lower (P < 0.05) concentrations of branched-chain volatile fatty acids (VFA) than those fed alfalfa silage. All VFA concentrations were higher (P < 0.001) in rumen liquor samples than in fecal samples. The interaction of sample type and diet showed a significant effect (P < 0.05) on the proportions of the bacterial community that were from the phyla Proteobacteria and Verrucomicrobia. Ambient temperature had an indirect effect (P < 0.05) on the phylum Firmicutes, as it affected its proportional balance. The bacterial population diversity in samples appeared to decrease concurrently with the ambient temperature. The phylum Firmicutes explained the first principal component at 64.83 and 42.58% of the total variance in rumen liquor and fecal samples, respectively. The sample type had a larger effect on bacterial communities than diet and temperature. Certain bacterial populations seemed to be better adapted than others to environmentally adverse conditions, such as less access time to nutrients due to higher motility and rate of passage of digesta caused by extreme temperatures, or antimicrobials such as tannins, possibly due to an influence of their biogeographical location within the gut.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".