Field and Greenhouse Bioassays to Determine Mesotrione Residues in Soil
Bibliographic record
Abstract
Whole-plant bioassays using sugar beet, lettuce, cucumber, green bean, pea, and soybean as test crops were used to detect mesotrione residues in the soil. The test crops were planted in soil treated with mesotrione in the field the previous year at rates of 0 to 560 g ai ha−1and in nontreated soil from the same field, with mesotrione added at concentrations of 0 to 320 μg kg−1. Experiments were conducted in the greenhouse for a 21-d period. Values for the dose giving a 50% response (I50) were predicted using a log-logistic nonlinear regression model. I50values (mean ± SE) of 8.6 ± 1.8, 14.9 ± 2.0, 29.8 ± 11.0, 41.6 ± 7.3, 52.9 ± 6.4, and 67.9 ± 30.3 g ai ha−1for sugar beet, lettuce, green bean, cucumber, pea, and soybean, respectively, indicate that these crops were effective bioassay test species for quantifying mesotrione residues. A greenhouse bioassay was a simple and sensitive tool to detect mesotrione at concentrations of less than 1.0 μg kg−1with sugar beet and lettuce being the most sensitive test species. The I50values for soil treated with known concentrations of mesotrione were lower than for field soil treated with mesotrione the previous year. Knowing the level of mesotrione residues in the soil, growers have flexibility in crop rotations following mesotrione use on corn. Growers can use this information to minimize risk of crop injury by choosing appropriate rotation crops that suffer little or no yield reduction.
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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) | 0.001 | 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 | 0.001 |
| 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".