Early life microbial exposure shapes subsequent animal health
Bibliographic record
Abstract
Biosecurity standards and farming practices have profoundly changed the way domestic animals interact with the environment and themselves. Farm intensification processes resemble the lifestyle changes that humans underwent post industrialization, which have been linked to the occurrence of immune-mediated and metabolic disorders. Modern rearing practices reduce maternal and offspring interactions, promote changes in diet, restrict animals indoors, and rely on the use of antibiotics and vaccines to maintain animal health. These practices may hinder the proper colonization of the gastrointestinal tract with commensal organisms that co-evolved with livestock species. The gut microbiota aids nutrient digestion, stimulates immune and intestinal development and maturation, and promotes the competitive exclusion of pathogens. Microbial colonization in early life is critical for host metabolic and immune programming, and disruptions of gut microbial community stability can lead to development of metabolic and immune disorders seen at later stages of life. Identifying how farming practices influence microbial composition and the potential effects on host physiology, metabolism, and disease resistance is necessary to guide intervention strategies to promote beneficial microbial–host interactions, and improve animal health and performance.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".