The Trends of Camel Research in North America: A Bibliometric Approach
Bibliographic record
Abstract
Camels are witnessing global attraction even in countries outside the natural camel habitat. This study aims to provide a comprehensive bibliometric analysis of camel research in North America, identifying key trends, influential contributors, and collaborative networks. Data was sourced from the Scopus database, yielding 786 papers on camel research affiliated with institutions in the United States and Canada. Bibliometric tools such as VOSviewer and R Studio’s Bibliometrix package were used to visualise and analyse the data. The bibliometric analysis of camel research in North America highlights substantial collaborative efforts between the North American countries and international partners. The data spans 493 sources, with an annual growth rate of 1.89%. The research involves 3127 authors and a notable international co-authorship rate of 52.04%. Camel research in North America spans multiple disciplines, including Agricultural and Biological Sciences, Medicine and Veterinary Sciences, Chemistry, Genetics and Molecular Biology, Immunology and Microbiology, Environmental Science, Engineering, and Social Sciences. This interdisciplinary approach underscores the broad impact and significance of camel studies. The international partnerships have facilitated significant advancements in understanding zoonotic diseases, genetic diversity, and the nutritional benefits of camel milk, underscoring the importance of maintaining robust international research networks to enhance the quality and impact of camel studies. This bibliometric analysis highlights the growing significance of camel research in North America, with strong international collaborations driving advancements. However, gaps remain in integrating advanced technologies and exploring socio-economic impacts in non-traditional regions. Continued investment and international collaboration are essential to address these gaps and drive innovative research initiatives.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.137 | 0.200 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".