Bibliometric analysis of strawberry ( <i>Fragaria × ananassa</i> Duch.) research from Plant Sciences category based on Web of Science
Bibliographic record
Abstract
Abstract Strawberry ( Fragaria × ananassa Duch.) is one of the most significant horticultural cash crops in the world. The study aimed to identify and analyse the 2,930 articles and review type papers of strawberry research from the Plant Sciences category based on the Web of Science. Papers mainly written in English (2,894, 98.771%), were from a total of 8,838 authors, 96 countries/territories, 1,845 organisations and published in 197 journals and book series. The top seven core journals are ranked as Plant Disease (252, 8.601%), Phytopathology (229, 7.816%), Plant Pathology (93, 3.174%), Frontiers in Plant Science (89, 3.308%), Canadian Journal of Plant Science (86, 2.935%), European Journal of Plant Pathology (86, 2.935%) and Journal of Experimental Botany (86, 2.935%), and these journals each published >86 papers. The top five countries and regions were the USA, People's Republic of China, Spain, Canada and England. The top five organisations were the University of Florida, USDA ARS, University of Malaga, University of California Davis, Agriculture and Agri-Food Canada. The top five authors are Peres, Natalia A. (Peres, Natalia; Peres, N.A.); Madden, LV; Munoz-Blanco, Juan (Munoz-Blanco, J); Schwab, Wilfried (Schwab, W) and Ellis, MA, each published >25 papers. All keywords of the strawberry research from the Plant Sciences category were separated into 11 clusters for different research topics. Visualisations offer exploratory information on the current state in a scientific field or discipline as well as indicate possible developments in the future. The review could provide a valuable guide for designing future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.152 | 0.154 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| 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".