Review of studies analysing glyphosate and aminomethylphosphonic acid (AMPA) occurrence in groundwater
Bibliographic record
Abstract
The herbicide glyphosate (N-phosphonomethylglycine) has been the most commonly applied herbicide worldwide for the past 40 years. This review presents and discusses the state of knowledge concerning groundwater contamination by glyphosate and its metabolite, aminomethylphosphonic acid (AMPA). A dataset of 48 reports and articles reporting on glyphosate and AMPA in groundwater worldwide was constructed from the published literature. Specific attention was given to the analysis of available studies on glyphosate and AMPA transport in groundwater to investigate groundwater contamination factors. The information presented in this review highlights detectable groundwater contamination in several countries, with many cases exceeding European groundwater quality standards. Both agricultural and non-agricultural applications of glyphosate can be significant sources of pollution, and proximity to agricultural fields is a significant risk factor. AMPA has two primary sources, glyphosate and amino polyphosphates, but very little information is available about the origin of AMPA detected in groundwater. Glyphosate and AMPA transport to groundwater can be significant in well-structured soils that are rich in macropores, where it generally occurs via preferential flow. An inverse relationship was highlighted between the groundwater depth and glyphosate and the occurrence and concentration of AMPA in groundwater; however, some discrepancies among the studies were identified. Heavy rainfall shortly after glyphosate application poses a high risk of its transport to groundwater. Seasonal trends in glyphosate and AMPA in groundwater, related to application timing, agronomic practices, and weather conditions, were also observed. Finally, risk-management measures are proposed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".