Role of reactive oxygen species in auxin herbicide phytotoxicity: current information and hormonal implications — are gibberellins, cytokinins, and polyamines involved?
Bibliographic record
Abstract
It has been suggested that reactive oxygen species (ROS) participate in the injury and death of sensitive plants treated with auxin herbicides. However, their precise role in the phytotoxicity of these compounds has not been completely elucidated. ROS might not only be essential for inducing epinasty, senescence, and tumours, but they might be crucial to the generation of ethylene, abscisic acid, and jasmonic acid, which are known to be triggered upon application of these compounds. Also, the main sources of ROS overproduction and their subcellular location in plants treated with auxin herbicides have not yet been clarified. Recent studies have suggested a role for xanthine oxidase (XOD) (which produces superoxide radical ([Formula: see text]) and has activity related to nucleic acid catabolism) and for peroxisomes in the oxidative stress and senescence induced by auxin herbicides in the leaves of sensitive plants. However, confirmatory studies at the molecular level are still needed, as well as studies on the possible involvement of other hormones, such as gibberellins, cytokinins, and polyamines, and their corresponding crosstalk with ROS, in the mode of action of auxin herbicides. The results from these studies could not only help to clarify the mechanism of phytotoxicity of these compounds, but also to enable the design of ecologically safer herbicides for agriculture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".