Understanding the gut microbiome of dairy calves: Opportunities to improve early-life gut health
Bibliographic record
Abstract
Early gut microbiota plays a vital role in the long-term health of the host. However, understanding of these microbiota is very limited in livestock species, especially in dairy calves. Neonatal calves are highly susceptible to enteric infections, one of the major causes of calf death, so approaches to improving gut health and overall calf health are needed. An increasing number of studies are exploring the microbial composition of the gut, the mucosal immune system, and early dietary interventions to improve the health of dairy calves, revealing possibilities for effectively reducing the susceptibility of calves to enteric infections while promoting growth. Still, comprehensive understanding of the effect of dietary interventions on gut microbiota-one of the key aspects of gut health-is lacking. Such knowledge may provide in-depth understanding of the mechanisms behind functional changes in response to dietary interventions. Understanding of host-microbial interactions with dietary interventions and the role of the gut microbiota during pathogenesis at the site of infection in early life is vital for designing effective tools and techniques to improve calf gut health.
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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".