Sorption and desorption of non‐ionic herbicides onto particulate organic matter from surface soils under different land uses
Bibliographic record
Abstract
Summary In vegetative filter strips used to intercept pesticides present in run‐off, particulate organic matter derived from the vegetation plays an important function in pesticide sorption processes, because it accumulates at the soil surface and quickly responds to changes in land use. Two herbicides with contrasted properties: isoproturon, moderately hydrophobic (log K ow = 2.5), diflufenican, strongly hydrophobic (log K ow = 4.9), and isopropylaniline, a metabolite of isoproturon, were used to characterize the sorption and desorption properties of POM originating from soils under three different land uses: a cropped plot under conventional wheat/maize rotation, an adjacent 10‐year‐old grassed strip and a nearby 80‐year‐old oak/chestnut forest. Chemical structural composition information obtained from solid‐state 13 C CPMAS NMR and estimation of hydrophobicity from contact angle measurements were used to explain the different sorption capacities of POM according to their size and origins. Sorption of isoproturon and diflufenican increased with hydrophobicity of POM, which was greater in the forest soil. Aromaticity of POM was positively correlated to sorption coefficients ( K oc ). Desorption of the more hydrophobic compounds, diflufenican and isopropylaniline was weak for all POM fractions, regardless of their origin and size. On the other hand, desorption of isoproturon depended on land use and POM characteristics. The sorption capacities of POM were not only controlled by their chemical composition, but also by their size, due to a greater number of sorptive sites related to a greater surface area with decreasing particle‐size.
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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 | 0.000 | 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) | 0.001 | 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".