Effect of the Presence of Nonionic Surfactant Brij35 on the Mobility of Metribuzin in Soil
Bibliographic record
Abstract
Given the water scarcity becoming endemic to a large portion of the globe, arid region irrigation has resorted to the use of treated, partially treated, or even untreated wastewaters. Such waters contain a number of pollutants, including surfactants. Applied to agricultural lands, these surfactants could affect the fate and transport of other chemicals in the soil, particularly pesticides. A field lysimeter study was undertaken to investigate the effect of nonionic surfactant, Brij35, on the in-soil fate and transport of a commonly used herbicide, metribuzin [4-amino-6-tert-butyl-3-(methylthio)-1,2,4-triazin-5(4H)-one]. Nine PVC lysimeters, 1.0 m long × 0.45 m diameter, were packed with a sandy soil to a bulk density of 1.35 mg m−3. Antibiotic-free cattle manure was applied (10 mg ha−1) at the surface of the lysimeters. Metribuzin was then applied to the soil surface of all lysimeters at a rate of 1.00 kg a.i. ha−1. Each of three aqueous Brij35 solutions, 0, 0.5 and 5 mg L−1 (i.e., “good”, “poor” and “very poor” quality irrigation water) were each applied to the lysimeters in triplicate. Analysis for metribuzin residues in samples of both soil and leachate, collected over a 90-day period, showed the surfactant Brij35 to have increased the mobility of metribuzin in soil, indicating that continued use of poor quality water could influence pesticide transport in agricultural soils, and increase the risk of groundwater contamination.
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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 | 0.001 |
| 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) | 0.001 | 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".