Exploring global trends in scientific research on <i>Rubus glaucus</i> Benth.: A comprehensive analysis integrating bibliometrics, LDA, and HJ-Biplot
Bibliographic record
Abstract
Background Rubus glaucus Benth, called Andean blackberry, is a species of significant economic and ecological importance. Despite its relevance, scientific research on this plant remains fragmented and scattered across disciplines. Objective This study aims to systematically assess the state of scientific knowledge on R. glaucus, identifying research trends, collaborations, and thematic evolutions within the global research community. Methods We employed a comprehensive bibliometric analysis integrated with Latent Dirichlet Allocation (LDA) and HJ-Biplot methodologies to analyze publications from Scopus and Web of Science databases. Results Our findings reveal a substantial increase in research interest from the 1990s, reaching a peak in the early 2010s before a recent decline. The study highlights significant contributions from the United States, the United Kingdom, Canada, Italy, and Colombia, with notable international collaborations. Thematic analysis underscored the ecological role, nutritional benefits, and genetic improvement of R. glaucus as focal areas of research, pointing out gaps in pest management and sustainable cultivation practices. Conclusions This comprehensive bibliometric analysis offers valuable insights into the research landscape of R. glaucus, underscoring the need for focused research efforts on underexplored areas. The study lays the groundwork for future research directions, encouraging interdisciplinary collaboration to leverage the plant's full potential for agricultural innovation.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.120 | 0.136 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".