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Record W2604245724 · doi:10.1139/cjb-2016-0084

Role of reactive oxygen species in auxin herbicide phytotoxicity: current information and hormonal implications — are gibberellins, cytokinins, and polyamines involved?

2017· article· en· W2604245724 on OpenAlexvenueno aff
Iva McCarthy-Suárez

Bibliographic record

VenueBotany · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAuxinReactive oxygen speciesJasmonic acidAbscisic acidGibberellinBiologyBiochemistryPhytotoxicityJasmonatePlant hormoneXanthine oxidaseCell biologyBotanyArabidopsisSalicylic acidEnzyme

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.236
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2017
Admission routes1
Has abstractyes

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