The Colonization and Establishment of the Neonatal Mammalian Microbiome
Bibliographic record
Abstract
In current agriculture practices, such as the dairy industry, the use of antibiotics is being discouraged due to the occurrence of antibiotic resistant bacteria. However, antibiotics are used commonly to treat calf diarrhea, which is a serious issue that negatively influences calf health, growth, and development. Recent research highlights the gut microbiota as a potential source to improve the gut health of a calf, which could minimize the antibiotic use. However, limited knowledge is available for the early life gut microbiota and its relationship with calf’s performance. It is known that the microbiota has an influence on immune system development, as well as behavioral development, and metabolic development. Further, an atypical microbial population, or a microbial shift, has been linked to autoimmune, anxiety and metabolic disorders. The process of microbial and host interactions starts at birth, suggesting that mammals are initially colonized by microbes immediately following and during birth. Differing modes of delivery, caesarian or vaginal delivery, and possibly the length of time of the birthing process, may determine initial colonization of the infant. Further, the establishment of the microbiota can be influenced by host genetics, diet, and maternal environment. Therefore, this review aims to summarize the current understanding of the neonatal mammalian microbiota obtained from human and mice studies, and to outline future research directions on microbial colonization and possible manipulation strategies that can be used to manipulate the gut microbiota in dairy calves. By understanding the process of how mammals and microbes interact it is possible to better target future research in order to solve the problem of calf diarrhea.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".