The carbon stable isotope compositions of glyphosate and aminomethylphosphonic acid (AMPA): Improved analytical sensitivity and first application to environmental water matrices
Bibliographic record
Abstract
Rationale The presence of glyphosate and its degradation product aminomethylphosphonic acid (AMPA) in the environment has adverse effects on environmental quality, raising the need to better constrain their fates, in particular the processes that control their production and degradation. Our aim was to improve the sensitivity of their δ 13 C analysis and demonstrate the feasibility of measuring them in natural surface water. Methods The δ 13 C values of dissolved glyphosate and AMPA were determined using isotope ratio mass spectrometry (IRMS) (Delta V Plus instrument) coupled to a high‐performance liquid chromatography (HPLC) unit, where glyphosate and AMPA were separated on a Hypercarb column. Results We demonstrated an improved sensitivity of the δ 13 C analysis for glyphosate and AMPA by LC/IRMS compared with previous studies. For waters from the carbonate and silicate hydrofacies, while no pretreatment was required for the isotope analysis of glyphosate, removal by H 3 PO 4 acidification of dissolved inorganic carbon, that co‐elutes with AMPA, was required prior to its analysis. We successfully tested a freeze‐drying pre‐concentration method showing no associated isotope fractionation up to concentration factors of 500 and 50 for glyphosate and AMPA, respectively. Conclusions We demonstrated, for the first time, the feasibility of measuring the δ 13 C values of glyphosate and AMPA in natural surface waters with contrasted hydrofacies (calcium carbonate and silicate types). This opens new fields in pesticide research, especially on the characterization of processes that control their degradation and the production of their secondary byproducts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".