Mapping the Intellectual Structure and Thematic Evolution of Dietary Fat Research in Ruminants: A Comprehensive Bibliometric Analysis
Bibliographic record
Abstract
Interest in dietary fat supplementation in ruminant nutrition has significantly evolved in recent decades.However, a comprehensive bibliometric analysis of this field is lacking.To address this gap, this study analyzed documents published up to 2024 using specific search terms in the Scopus database.450 publications were identified and analyzed using the bibliometrix R-package and VOSviewer software, incorporating key bibliometric techniques including co-authorship analysis, keyword co-occurrence, co-citation analysis, and thematic and topic evolution mapping.The analysis revealed that the majority of the literature consists of research articles, focusing on both fundamental biochemical studies and practical applications in dairy and meat production.Core journals such as the Journal of Dairy Science, Journal of Animal Science, and Animal Feed Science and Technology have been pivotal in shaping this field.The number of publications has steadily increased, particularly in recent years, reflecting a shift towards integrating nutritional strategies with environmental sustainability.Institutions from North America and Europe have dominated the research output.Emerging themes for future research include "alternative fat sources" and "methane mitigation," highlighting a growing focus on sustainable practices that reduce the environmental footprint of ruminant production while enhancing the health quality of animal products.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.179 | 0.202 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".