Gut Health in Veterinary Medicine: A Bibliometric Analysis of the Literature
Bibliographic record
Abstract
Gut health is a recent relevant research topic in veterinary medicine and it has been shown to be associated with better zootechnical performances and animal welfare. A bibliometric analysis was performed to explore publication trends, dynamics and possible knowledge gaps in this field in the last twenty years (2000-2020). The database Web of Science was searched and the results were analyzed by the R package Bibliometrix. A total of 1696 documents were retrieved in the evaluated period, showing a constant annual growth in the number of publications of 22.4%. These articles focused mainly on pigs (34.8%), poultry (chicken, duck, turkey and quail-33.9%) and aquaculture (fishes, crustaceans and frog-15.0%) while a scarce number was found on felines, cows, horses, rodents, goats and sheep. China was the most productive country (24.7%) followed by the USA (17.2%) and Canada (5.7%). Keyword's analysis showed that the main research lines aimed to explore animal nutrition, prevention of inflammatory diseases and microbiota composition. This study shows a comprehensive bibliometric analysis of the evolution of veterinary gut health research that will help to drive future investigations on this topic.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.147 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".