Global research production in glyphosate intoxication from 1978 to 2015: A bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Glyphosate (N-phosphonomethylglycine) has been used as a broad-spectrum herbicide that has been widely used in the agricultural industry and also available for home use. The main aim of this study is to present a general overview of glyphosate intoxication-related publications from its introducing since the early 1970s using bibliometric technique. METHODS: On June 23, 2016, a literature search of the Scopus database was performed. We then extracted and analyzed the data using well-established qualitative and quantitative bibliometric indices: Publication year, affiliation, document type, country name, subject category, journal name, publishing language, and collaboration and citation patterns. RESULTS: We recognized a total of 3735 publications on glyphosate published between 1973 and 2015. There were 875 publications related to glyphosate intoxication in the Scopus database published between 1978 and 2015. Articles (757) comprised 86.5% of the total publications, followed by reviews (41; 4.7%). Most publications were published in English (87.9%), followed by Portuguese (6.6%). The number of publications related to glyphosate intoxication increased from 44 in 1978-1987 up to 152 in 1996-2005 and then quadrupled in 2006-2015. The United States was the leading country with 180 documents representing 20.6%, followed by Brazil (120; 13.7%), Canada (78; 8.9%), Argentina (61; 7.0%), and France (57; 6.5%). The 85.6% of the publications was cited, and the average of citation per document was 17.13 with h-index of 55. Furthermore, the United States achieved the highest h-index of 33. Most of the global international collaborations are made with researchers from the United States, who collaborated with 23 countries/territories in 44 publications. CONCLUSIONS: The trends in global glyphosate-related research between 1978 and 2015 were evaluated by a bibliometric technique. Results showed that English was the leading publishing language, and the major publication type was original article. Findings showed that number of research publications related to glyphosate intoxication increased significantly in the last decade. The United States and Brazil are the two most productive countries in research on glyphosate intoxication. This study will be beneficial to policy makers by identifying areas that need greater investment and research funding to target appropriate agriculture sectors so as to improve glyphosate safety in a global setting.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.126 | 0.176 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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, 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".