On the use of probiotics to improve dairy cattle health and productivity
Bibliographic record
Abstract
Probiotics are genetically identifiable, live microorganisms that when administered in adequate amounts, confer appropriately sized health benefit (e.g. correcting dysbiosis, immunomodulatory effect) on a target host. In cattle, probiotics have shown promising results and long-term benefits in productivity when used on animals under stress. The health and production benefits of probiotics were attributed to improvement in fermentation in rumen and intestine, the stabilisation of rumen pH, and improvements in the intestinal barriers. In the bovine udder, a dysbiosis of the commensal intramammary microbiota and the presence of mastitis causing-bacteria has been linked to increased intramammary infections. Probiotic bacteria capable of biofilm formation inside the udder either serve as a barrier against pathogens or disrupt and replace biofilms formed by pathogens. Over the past two decades, several types of probiotics have been used as feed additives; however, the effect of probiotic use on disease prevention and cattle health and performance indicators, and characterisation of the immunomodulatory association between probiotic microbiota and host target system microbiota are yet to be quantified or documented.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".