The Colonization and Establishment of the Neonatal Mammalian Microbiome
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
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.
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.
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 | 0.000 |
| Science and technology studies | 0.001 | 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