Comparison of the effects of glyphosate and atrazine herbicides on nontarget plants grown singly and in microcosms
Bibliographic record
Abstract
Intensification of agriculture and the corresponding increase in herbicide use has led to concern regarding the effects these chemicals may have on nontarget plants of agroecosystems. Current pesticide registration guidelines are focused on testing crop species grown singly in pots under greenhouse conditions and may not provide adequate measures of protection to noncrop species. The objective of the present study was to compare the response of terrestrial and wetland plants to the herbicides glyphosate and atrazine when grown singly in pots versus under different microcosm conditions. Greenhouse microcosms were generally more sensitive than single-species tests. Plants grown for an extended test period or in seminatural field conditions were generally less sensitive to herbicides. Sensitivity was found to be dependent on interactions between species and test conditions. Changes in community structure were observed in herbicide-treated microcosms that would not be predicted from single-species testing. Single-species tests are useful because they are inexpensive, can demonstrate clear dose-response patterns uncomplicated by other factors influencing growth, and are able to provide a measure of the sensitivity of a given species to glyphosate and atrazine. However, they are unable to predict subtle changes in community structure that may have important long-term consequences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".