Impact of Grass and Grass with Poplar Buffer Strips on Atrazine and Metolachlor Losses in Surface Runoff and Subsurface Infiltration from Agricultural Plots
Bibliographic record
Abstract
In many areas of intensive corn production, atrazine and metolachlor are among the most commonly found herbicides in surface and ground water. This 2-yr study compared the impact of grass and grass+tree buffer strips on the exported masses of atrazine, metolachlor, and a degradation product of atrazine, desethylatrazine (DEA). The experimental system consisted of four replicate plots in a three-way completely randomized design (no buffer zone, grass buffer zone, and grass+tree buffer strips). The field plots were 5 m wide and 30 m long and grown in corn. The grass and grass+tree buffer strips were 5 m and had the same grass vegetation except for eight young hybrid poplars. Over the 2-yr study, surface runoff and subsurface infiltration water (under the buffer strip) were collected after the initial three rainfall events after herbicide application. Dissolved atrazine, metolachlor, and DEA were analyzed by gas chromatography/mass spectrometry. The presence of buffer strips decreased the exported masses of atrazine and metolachlor in surface runoff. A three-way ANOVA with treatment (type of buffer strip), water (surface runoff or subsurface infiltration), and time between herbicide application and rainfall event as factors showed a significant reduction (40-60% in 2004 and 75-95% in 2005) in the total (surface runoff+infiltrated water) exported masses of atrazine and metolachlor in the presence of buffer strips. Rainfall events after herbicide application were different between the 2 yr and greatly affected the flow distribution (e.g., subsurface infiltration) and the leached herbicide concentrations. No significant difference in the capacity to reduce herbicide exports was observed between grass and grass+tree buffer strip treatments; the poorly developed young poplar biomass at the time of the study may partly explain this observation.
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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 | 0.001 |
| 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".