Differences in the fecal microbiota of dairy calves reared with differing sources of milk and levels of maternal contact
Bibliographic record
Abstract
The practice of rearing cows and calves together is gaining popularity on dairy farms, with different systems currently under assessment in mainland Europe, the United Kingdom, and Oceania. Research into the effects of cow–calf rearing has primarily focused on direct health and welfare implications, and little work has examined the role of different rearing paradigms on calf microbiota. We trialed a cow–calf rearing system on a Canadian dairy farm and compared fecal microbiota of these calves with the microbiota of calves reared according to the conventional practice of the same farm (separated from the dam and fed waste milk). At 4 wk, the conventionally reared calves had reduced relative abundance of Lactobacillus and higher relative abundance of other taxa, including Sutterella , Prevotella , and Bacteroides. We also detected predicted functional differences, such as reduced l-tryptophan biosynthesis in conventionally reared calves. These results suggest that maternal contact may influence the calf microbiota, but the observed differences are also likely related to other aspects of the rearing environment independent of maternal contact (e.g., potential exposure to antibiotic residues in waste milk). These findings provide preliminary evidence of the effects of early rearing environments on the establishment of the dairy calf fecal microbiota. This research is needed, given the critical role of the bovine gut microbiome in behavioral, metabolic, and immune development.
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 | 0.000 |
| 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".