Herbicidal effects on nontarget vegetation: Investigating the limitations of current pesticide registration guidelines
Bibliographic record
Abstract
The impact of herbicide exposure on nontarget vegetation within agroecosystems has sparked extensive research that revealed that current pesticide registration guidelines may be inadequate at predicting the effects of herbicides on wild plants and habitats. This study extends the current interest by presenting three experiments highlighting some of the limitations to current phytotoxicity testing guidelines. Several crops and wild plant species were grown under greenhouse conditions following standard protocol for phytotoxicity testing. Plants were sprayed with five different herbicides at the four- to six-leaf stage, and biomass was recorded at 28 d after spray. Results showed that current regulatory protocol will likely underestimate herbicide phytotoxicity if testing does not include data for the complete tank-mix formulation. The present study also showed that the range in herbicide sensitivity among cultivars of the same crop can be quite extensive and that, depending on the cultivar included in a risk assessment, conclusions regarding the phytotoxicity of any given herbicide may differ. Although no significant differences in sensitivity were found between crops and related wild species, results revealed that current guidelines are too rigid in terms of species selection. Considering the variability among crop cultivars, coupled with the ecological importance and the ease of germination of many noncrop plant species, pesticide regulatory guidelines would be improved if wild species were included in testing. Findings of the present study indicate that current pesticide regulatory guidelines require modifications to ensure a more accurate assessment of herbicide effects on nontarget plant species.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".