Updates and Current Challenges in Reproductive Microbiome: A Comparative Analysis between Cows and Women
Bibliographic record
Abstract
The microbiota plays an important role in numerous physiological processes, pathogenesis, development, and metabolism in different animal species. In humans, several studies have demonstrated an association between the vaginal microbiota and fertility rates, and even success in assisted reproduction techniques. In the context of cattle reproduction, although few studies have addressed the microbiota in a healthy state (which is not associated with diseases that affect the reproductive tract of cows), changes in its composition also seem to influence fertility. This review aims to explain the importance of the reproductive microbiota in female bovines and what is available in the literature regarding its possible role in increasing fertility. What are the challenges involved in this process? Future perspectives on its use and manipulation as a selection or intervention tool. Will it be possible to one day extrapolate the findings to reality and apply them in the field? In short, understanding the role of the reproductive microbiota of female bovines can signal the prospect of increasing production, whether of milk or meat, from the same number of animals, as it can optimize reproductive efficiency and perhaps become an allied tool for the economic profitability and sustainability of livestock farming.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".