Leaching of Three Sulfonylurea Herbicides during Sprinkler Irrigation
Bibliographic record
Abstract
Sulfonylurea herbicides are widely applied on the Canadian prairies to control weeds in a variety of crops. Several sulfonylurea herbicides are mobile in soil, and there is concern about their possible movement to ground water. This study was performed to assess the susceptibility of three sulfonylurea herbicides commonly used in prairie crop production to leach under a worst-case scenario. Thifensulfuron-methyl, tribenuron-methyl, and rimsulfuron were applied to a 9-ha tile-drained field, and then approximately 300 mm of irrigation water were applied over a 2-wk period using a center pivot. The commencement of tile-drain flow corresponded to the rise of the water table above tile-drain depth, and peak flow rates corresponded to the greatest depths of ground water above the tile drains. The volume of irrigation water intercepted by the tile drains in each quadrant was determined by site hydrology and represented <10% of the irrigation water applied. Concentrations of thifensulfuron-methyl, tribenuron-methyl, and rimsulfuron in the tile-drain effluent ranged (analysis by liquid chromatography/tandem mass spectrometry) from 2.0 to 248 ng L(-1), not detected (nd) to 55 ng L(-1), and nd to 497 ng L(-1), respectively. Total herbicide transport from the root zone in each quadrant was estimated at <0.5% of the amount of each sulfonylurea herbicide applied. Thifensulfuron-methyl was the only herbicide detected in ground water, with concentrations ranging from 1.2 to 2.5 ng L(-1). With the frequency and amount of rainfall typically encountered in the prairie region of Canada, detectable concentrations (>1 ng L(-1)) of these sulfonylurea herbicides in ground water would be unlikely.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".