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Record W4412815401 · doi:10.5376/jeb.2025.16.0007

Role of Reactive Oxygen Species in Potato's Stress Response

2025· article· en· W4412815401 on OpenAlexvenueno aff
Qian Zhang, Wenzhong Huang

Bibliographic record

VenueJournal of Energy Bioscience · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsReactive oxygen speciesFight-or-flight responseStress (linguistics)OxygenChemistryBiochemistryLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

This study focuses on the role of reactive oxygen species (ROS) when potatoes encounter stress. ROS is a signaling molecule present in plants. When plants face external stress (such as environmental changes or pests and diseases), ROS plays an important role. However, it also has certain counter-effects. If the content of ROS in plants is too high, it will cause oxidative stress and damage cells. In order to control the amount of ROS, potatoes will activate some antioxidant enzymes, such as superoxide dismutase (SOD), catalase (CAT) and peroxidase (PRXs), which can remove excess ROS and protect cells from damage, while also making plants more resistant to stress. ROS will participate in regulating responses under stress such as drought, high salt, high or low temperature environments, as well as pathogens and leaf-eating insects. It not only helps to transmit stress signals, but also cooperates with some plant hormones (such as ABA, SA and JA) to regulate the plant's defense mechanism. Through this study, we also found that there are interactions between ROS signals and other signaling pathways (such as calcium signals). The study also discusses how to use this knowledge to improve potato's stress resistance.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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