Small‐Scale Spatial Variability of Atrazine and Dinoseb Adsorption Parameters in an Alluvial Soil
Bibliographic record
Abstract
Soil sorption processes largely control the environmental fate of herbicides. Therefore, accuracy of sorption parameters is crucial for accurate prediction of herbicide mobility in agricultural soils. A combined experimental and statistical study was performed to investigate the small-scale spatial variability of sorption parameters for atrazine and dinoseb in soils and to establish the number of samples needed to provide a value of the distribution coefficient (K(d)) next to the mean, with a given precision. The study explored sorption properties of the two herbicides in subsurface samples collected from four pits distributed along a transect of an alluvial soil; two to four samples were taken at about 30 cm apart at each sampling location. When considering all the data, the distribution coefficients were found to be normally and log-normally distributed for atrazine and dinoseb, respectively; the CVs were relatively high (close to 50% for dinoseb and 40% for atrazine). When analyzed horizon by horizon, the data revealed distribution coefficients normally distributed for both herbicides, whatever the soil layer, with lower CVs. The K(d) values were shown to vary considerably between samples collected at very short distance (a few centimeters), suggesting that taking a single soil sample to determine sorption properties through batch experiments can lead to highly unrepresentative results and to poor sorption/mobility predictions.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".