Mass spectrometry of the photolysis of sulfonylurea herbicides in prairie waters
Bibliographic record
Abstract
This review of mass spectrometry of sulfonylurea herbicides includes a focus on studies relevant to Canadian Prairie waters. Emphasis is given to data gaps in the literature for the rates of photolysis of selected sulfonylurea herbicides in different water matrices. Specifically, results are evaluated for positive ion electrospray tandem mass spectrometry with liquid chromatography separation for the study of the photolysis of chlorsulfuron, tribenuron-methyl, thifensulfuron-methyl, metsulfuron-methyl, and ethametsulfuron-methyl. LC-MS/MS is shown to be the method of choice for the quantification of sulfonylurea herbicides with instrumental detection limits ranging from 1.3 to 7.2 pg (on-column). Tandem mass spectrometry coupled with the use of authentic standards likewise has proven to be well suited for the identification of transformation products. To date, however, the power of time-of-flight MS and ultrahigh resolution MS has not been exploited fully for the identification of unknown photolysis products. Dissipation of the herbicides under natural sunlight fit pseudo-first-order kinetics with half-life values ranging from 4.4 to 99 days. For simulated sunlight, radiation wavelengths shorter than 400 nm are required to induce significant photolytic reactions. The correlation between field dissipation studies and laboratory photolysis experiments suggests that photolysis is a major pathway for the dissipation of some sulfonylurea herbicides in natural Prairie waters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".