Dissipation of glyphosate and aminomethylphosphonic acid in water and sediment of two Canadian prairie wetlands
Bibliographic record
Abstract
Glyphosate [N-(phosphonomethyl)glycine] is the active ingredient of several herbicide products first registered for use in 1974 under the tradename Roundup. The use of glyphosate-based herbicides has increased dramatically over the last two decades particularly in association with the adoption of glyphosate-tolerant crops. Glyphosate has been detected in a range of surface waters but this is the first study to monitor its fate in prairie wetlands situated in agricultural fields. An ephemeral wetland (E) and a semi-permanent wetland (SP) were each divided into halves using a polyvinyl curtain. One half of each wetland was fortified with glyphosate with the added mass simulating an accidental direct overspray. Glyphosate dissipated rapidly in the water column of the two prairie wetlands studied (DT(50) values of 1.3 and 4.8 d) which may effectively reduce the impact of exposure of aquatic biota to the herbicide. Degradation of glyphosate to its major metabolite aminomethylphosphonic acid (AMPA) and sorption of the herbicide to bottom sediment were more important pathways for the dissipation of glyphosate from the water column than movement of the herbicide with infiltrating water. Presently, we are not aware of any Canadian guidelines for glyphosate residues in sediment of aquatic ecosystems. Since a substantial portion of glyphosate entering prairie wetlands will become associated with bottom sediments, particularly in ephemeral wetlands, guidelines would need to be developed to assess the protection of organisms that spend all or part of their lifecycle in sediment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".