Molecular Detection of Age-Related Abundance and Diversity of Bifidobacterium spp. in Pastured Goats
Bibliographic record
Abstract
Bifidobacterium spp. are among bacteria being developed as probiotics for farm animals. Understanding diversity of the normal gut inhabitants is a prerequisite to developing species-specific probiotics strains that are likely to establish in the host. For many farm animals including goats, the normal gut Bifidobacterium inhabitants have not been characterized including age based differences. The objective of this exploratory study was to gain preliminary understanding of abundance and diversity of Bifidobacterium species in goats of different ages. Molecular methods have given scientists fast methods for exploratory insights into previously uncharacterized microbiomes. Consequently, in this study we utilized molecular assays using genus and species specific primers to characterize Bifidobacterium in pastured goats. Although Bifidobacterium were detected in all ages, younger animals had higher counts than older goats. In goats below six months of age, B. angulatum, B. dentium, B. gallicum, B. animalis sub animalis, B. longum and B. catenulatum were detected. In goats six months and older, B. dentium and B. gallicum were predominant. In some goats however, the specific strains could not be identified with the currently available primers indicating there are goat specific unique strains that are yet to be characterized. Further research to characterize and isolate Bifidobacterium in goats are needed for future probiotic applications. In conclusion, Bifidobacterium spp. were common in all age groups of pastured goats but more abundant in pre-weaned goats compared to goats over six months. In addition age related differences in the diversity of Bifidobacterium spp. in goats were reported.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".