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.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.207 | 0.207 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".