The spatial variability of soil properties and 2,4-D sorption and desorption in two calcareous prairie landscapes
Bibliographic record
Abstract
Pesticides are widely used worldwide to increase agricultural crop yields, in which most of the applied pesticides are herbicides. Following application, herbicide sorption and desorption are the two most important processes controlling the herbicide's behaviour in the environment. This study examined the effects that soil properties and tillage have on the spatial variability of 2,4-Dichlorophenoxyacetic acid (2,4-D) sorption and desorption which may be useful for landscape based agricultural management and for pesticide fate modeling. The two agricultural sites were a heavily-tilled eroded hummocky landscape near Morris, Minnesota and a glacial till undulating landscape near Miniota, Manitoba. The herbicide sorption and desorption experiments were determined by the standard batch equilibrium method which used 14C labeled 2,4-D solutions. Both landscapes were delineated into landform element complexes (LECs) by a landform description model for describing the spatial distribution of the soil properties and herbicide behaviour within the landscapes. In the Morris site 100, 138, and 63 sampling points were allocated to upper, middle, and lower LECs, which encompassed 33%,46%, and 21% of the 301 points, respectively. At Miniota a total of 51, 72, 73, and 14 sampling points were allocated to the upper, middle, lower, and depressional LECs, which comprised 24%, 34%, 35%, and 7% of the 210 points, respectively. However, concerns were raised about microbial degradation of the herbicide during the sorption experiments, thus a preliminary study was first conducted to determine the effects of soil sterilization by mercuric chloride on the sorption of the 2,4-D by soil. Results indicated the mercuric chloride strongly decreased the capacity of the soil to retain herbicides and that the interference of the mercuric chloride with herbicide sorption increased with increasing soil organic carbon contents...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".