Ten years of ranavirus research (2010–2019): an analysis of global research trends
Bibliographic record
Abstract
Ranaviruses are large nucleocytoplasmic DNA viruses that infect ectothermic vertebrates. Here we report the results of a scientometric analysis of the field of ranavirology for the last 10 years. Using bibliometric tools we analyse trends, identify top publications and journals, and visualise the ranavirus collaboration landscape. The Web of Science core collection contains 545 ranavirus-related publications from 2010 to 2019, with more publications produced every year and a total of 6830 citations. Research output is primarily driven by the United States and People’s Republic of China, who together account for more than 60% of ranavirus publications. We also observed a positive correlation between the average number of co-authors on ranavirus publications and the year of publication, indicating that overall collaboration is increasing. A keyword analysis of ranavirus publications from 2010 to 2019 reveals several areas of research interest including; ecology, immunology, virology/molecular biology, genetics, ichthyology, and herpetology. While ranavirus research is conducted globally, relatively few publications have co-authors from both European and Asian countries, possibly because closer countries (geographical distance) are more likely to share co-authors. To this end, efforts should be made to foster collaborations across geopolitical and cultural boundaries, especially between countries with shared research interests as ultimately, understanding global pathogens, like ranaviruses, will require global collaboration.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.048 | 0.100 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".