Compost Tea as Organic Fertilizer and Plant Disease Control: Bibliometric Analysis
Bibliographic record
Abstract
A variety of research reports that compost tea controls plant pathogens and improves plant nutrition and plant growth. Therefore, it can be used to reduce the use of synthetic fertilizers and pesticides. The aim of the study was to characterize and quantify the scientific production in the SCOPUS database on compost tea using bibliometric indicators. A total of 285 published papers related to compost tea were identified. The results show a general increasing trend from 2001 to 2023, with the highest number of publications occurring in 2021. Most of the publications were in the form of original articles, and English was the main language of publication. The top 10 countries with the highest scientific productivity were the United States, Egypt, Spain, Canada, Italy, India, China, Australia, Iran and Malaysia. Zaccardelli, M. and Pane, C. were the authors with the highest productivity with nine articles. In the co-authorship networks, two main networks were registered: the first with Diáñez F., together with Gea F. J., Navarro M.Y. and Santo M., and the second with Zaccardelli M., Celono G., and Pane C. Therefore, the need to adapt more resilient agricultural production systems allows for the consideration of compost tea as an alternative to mitigate environmental problems and soil degradation.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.158 | 0.229 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".