Editorial: The role of stress proteins in plants under abiotic stress
Bibliographic record
Abstract
analysis, the stress-associated protein, the stress-activated protein kinase, late embryogenesis abundant protein Editorial on the Research TopicThe role of stress proteins in plants under abiotic stress Extreme environmental conditions pose significant challenges to plant survival and productivity.Over time, plants have evolved a myriad of defense mechanisms to mitigate the detrimental effects of adverse conditions.These mechanisms induce the production of a plethora of protective proteins, which play a crucial role in preserving the normal physiological and biochemical processes of plants under stress.In-depth investigation of these stress proteins may uncover significant insights into their potential applications in agricultural production.Various stress proteins have been identified, such as late embryogenesis abundant proteins (LEA proteins, including dehydrins), reactive oxygen species (ROS) scavenging enzymes, heat shock proteins (HSPs) that serve as molecular chaperones to preserve enzyme activity and protein structure, and enzymes responsible for maintaining plant redox status and eliminating oxygen free radicals.Current research has delved into the significance and mechanisms of these stress proteins, expanding our understanding of both their depth and scope.This knowledge has been extensively applied to the study of numerous plant species, ultimately contributing to the enhancement of agricultural production and improvement of ecological environments in the long term.This Research Topic has been meticulously organized, featuring three original research articles and one comprehensive review, designed to emphasize recent advancements in the following areas: (1) in-depth functional analysis of stress proteins, (2) regulatory networks of stress proteins, (3) bioinformatics analysis of stress proteins within the context of big data, and the cross-species evolutionary relationships of stress proteins.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".