A bibliometric analysis of research on blackberry micropropagation
Bibliographic record
Abstract
Abstract To date there is a growth in fresh and processed blackberry consumption and consequently in their cultivation. The increase in blackberry cultivation increases the demand for quality nursery material for new grove planting. Among plant propagation methods, micropropagation is gaining increasing interest because it allows to overcome some of the disadvantages associated with traditional agamic propagation methods, mostly cuttings and grafting. Several articles about blackberry micropropagation were published despite a comprehensive bibliometric review of the scientific literature. Therefore, the present article analyzed the papers in the Web of Science and Scopus on “blackberry micropropagation” to deepen the published scientific documents' evolution, trends, and macroareas. Analyzed parameters included subject and category areas, core sources of publication, country of publication, document type, language of publication, publication output, authorship, distribution of author keywords and most-frequently cited article. A total of 78 scientific documents in the field of blackberry micropropagation were found with the research strategy adopted from 1998 to 2024. The principal WoS categories were Horticulture, Agronomy, and Agriculture Multidisciplinary while in Scopus they were Agricultural and Biological Sciences, Veterinary and Biochemistry, Genetics and Molecular Biology. Brazil and Romania are the countries with the most publications in both databases and the authors with the highest number of documents are affiliated with Brazilian, Serbian, Canadian, and Romanian institutions. The present bibliometric analysis provides a comprehensive overview on the scientific publications on blackberry micropropagation. The findings of this study highlight the multidisciplinary nature of blackberry micropropagation research, involving contributions from various scientific disciplines.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.166 | 0.229 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".