GLYPHOSATE (N-(PHOSPHONOMETHYL)GLYCINE) CONCENTRATIONS IN WATER COURSES – SYSTEMATIC REVIEW AND SCIENTOMETRIC ANALYSIS
Bibliographic record
Abstract
Glyphosate, which degrades into aminomethylphosphonic acid (AMPA), is the most widely used active ingredient in herbicides worldwide. Both compounds can enter aquatic systems through surface runoff, leaching, spray drift, and irrigation, leading to water contamination and subsequent incorporation into the food chain. This study aimed to perform a systematic review and scientometric analysis of research published between 2015 and 2025 on glyphosate and AMPA concentrations in surface and groundwater, and to compare geographically detected concentrations with national regulatory thresholds. A systematic review was conducted following the PRISMA protocol, complemented by scientometric analysis. Literature searches were performed in the Web of Science, PubMed, ScienceDirect, and SciELO databases. A total of 127 articles reporting glyphosate and AMPA concentrations in surface and groundwater were selected. The countries contributing the largest number of studies were Argentina, Brazil, Canada, the United States, Mexico, and Italy. Scientometric analysis revealed that these nations not only dominate research output but also constitute the most influential co-citation networks, with the most frequently cited study originating from the United States. The highest concentration reported was in Brazil (8,700 µg/L), which is 133 times above the Brazilian regulatory limit (65 µg/L). Statistical analyses further showed that glyphosate concentrations vary significantly by geographic region, with notable differences between Europe and North America. Glyphosate concentrations frequently exceed national maximum permissible limits, even in countries with stringent legislation such as those in Europe, where values surpassed the legal threshold of 0.1 µg/L at multiple sites. These findings underscore the widespread nature of glyphosate contamination and highlight the need for stronger monitoring and regulatory enforcement.
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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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.077 | 0.062 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".