Importance of Soil Organic Matter Fractions in Soil‐Landscape and Regional Assessments of Pesticide Sorption and Leaching in Soil
Bibliographic record
Abstract
Agricultural policy frameworks aim to develop scientifically sound measures that can be used to assess the environmental performance and risks associated with agricultural systems. As part of this assessment, pesticide leaching models are applied at large scales to assess the risk of pesticide groundwater contamination across soil series, agricultural fields, watersheds, or regions. Measurements of pesticide sorption by soil are among the most sensitive input parameters in pesticide leaching models. Soil organic matter (SOM) is the single most important soil constituent influencing pesticide sorption in soils. The interaction of pesticides with SOM is often studied in the laboratory using batch‐equilibrium experiments in combination with techniques that quantify chemical and structural characteristics of SOM. This paper reviews these laboratory studies and discusses their importance to the development of agricultural policy frameworks. This review paper was written as part of a symposium on “Meaningful pools in determining soil C and N dynamics” which was held by the SSSA and the Canadian Soil Science Society during the 2004 ASA‐CSSA‐SSSA International Annual Meetings in Seattle, WA.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".