Research Trends and Development Patterns in Microgreens Publications: A Bibliometric Study from 2004 to 2023
Bibliographic record
Abstract
This article presents a general overview of scientific publications in the field of microgreens using bibliometric tools. Data were collected from the Web of Science database (from Clarivate Analytics) in the period from 2004 to 2023, covering 20 years of scientific publications. The results are presented in the form of tables, graphs, and charts to analyze the development of microgreens publications. The countries with the greatest influence on the microgreens topic are the USA, Italy, and India, which have the highest number of publications in the analyzed period with 133, 76, and 38 publications, respectively. On the other hand, the authors with the highest number of publications are Raphael, Y. (University Naples Federico II-Italy), De Pascale, S. (University Naples Federico II-Italy), and Luo, Y. (ARS, Food Quality Laboratory, Environmental Microbial & Food Safety Lab, USDA-USA). The journals with the highest productivity in microgreens are HortScience (American Society of Horticultural Science), Horticulturae (MDPI), and Foods (MDPI), with publication numbers of 49, 27, and 23, respectively. Regarding the relationship of the documents in this study with United Nations Sustainable Development Goals (SDGs), the large majority of documents can be linked to SDG 2 (Zero Hunger), followed by SDG 13 (Climate Action) and SDG 3 (Good Health and Well Being). As a final remark, the mapping, trends, and findings in this work can help to establish logical paths for researchers in the field of microgreens.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.072 | 0.144 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".