317 Microbial interventions to improve gut health in neonatal ruminants
Bibliographic record
Abstract
Abstract Colonization and establishment of a balanced and healthy gut microbiome during the neonatal period can directly and indirectly influence animals by affecting the development and metabolism, nutrition absorption, barrier and immune functions, and endocrine and neuron transmitter secretions. Therefore, neonatal period represents a crucial window of time in animal’s life that induces long-term developmental and immune memory. Therefore, alterations in the early gut microbial composition and colonization trajectories lead to long-term negative effects on animals’ production and health. While the rapidly developing gut microbial community is affected by various external factors due to its instability, it provides a great opportunity for microbial intervention to alter microbial colonization trajectories and their subsequent impact on gut health. Recent advance research has been successful in restoring altered gut microbial communities by using microbial interventions such as vaginal seeding, fecal microbial transplantation, probiotics, and prebiotics. However, there is a lack of understanding on the long-term effects of these interventions on gut health in neonatal ruminants. Direct fed microbes (live naturally existing microbes that can improve health and production performance) with psychobiotic function (a type of probiotic that affect cognitive and behavioral functions of the host via the gut-brain axis) can be one of the novel microbiome solutions to target and alter the microbiome dysbiosis. The use of direct fed microbes, postbiotics and psychobiotics and their potential implications in improving calf health and productivity leads to a novel solution to the manipulation of the gut microbiome in calves.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".