Trace level determination of selected sulfonylurea herbicides in wetland sediment by liquid chromatography electrospray tandem mass spectrometry
Bibliographic record
Abstract
Sulfonylurea herbicides are widely used in crop production on the Canadian prairies and a portion of these herbicides applied to cropland are inevitably lost to surrounding aquatic ecosystems. Little is known regarding the presence of sulfonylurea herbicides in wetlands located amongst cropland. This paper describes a new analytical method for the extraction and the determination of seven sulfonylurea herbicides (thifensulfuron-methyl, tribenuron-methyl, ethametsulfuron-methyl, metsulfuron-methyl, rimsulfuron, nicosulfuron and sulfosulfuron) in wetland sediment. The method provided > 85% analyte recovery from fortified sediment for six of the seven sulfonylurea herbicides with a limit of quantification (LOQ) of 1.0 microg kg(-1). Tribenuron-methyl had significantly lower recovery compared to the other six sulfonylurea herbicides (LOQ = 2 microg kg(-1)). Mean recovery standard deviations were < 10%. This methodology was used to quantify sulfonylurea herbicide residues in sediment samples collected from prairie wetlands situated within the agricultural landscape of Saskatchewan and Manitoba, Canada. This is the first-known detection of sulfonylurea herbicide residues in prairie wetland sediments. Ethametsulfuron-methyl, sulfosulfuron and metsulfuron-methyl, the three most environmentally persistent of the seven sulfonylurea herbicides monitored in the surveillance component of this study, were most frequently detected in wetland sediment with mean concentrations ranging from 1.2 to 10 microg kg(-1).
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".