Temperature regulation of plant hormone signaling during stress and development
Bibliographic record
Abstract
Global climate change has broad-ranging impacts on the natural environment and human civilization. Increasing average temperatures along with more frequent heat waves collectively have negative effects on cultivated crops in agricultural sectors and wild species in natural ecosystems. These aberrantly hot temperatures, together with cold stress, represent major abiotic stresses to plants. Molecular and physiological responses to high and low temperatures are intricately linked to the regulation of important plant hormones. In this review, we shall highlight our current understanding of how changing temperatures regulate plant hormone pathways during immunity, stress responses and development. This article will present an overview of known temperature-sensitive or temperature-reinforced molecular hubs in hormone biosynthesis, homeostasis, signaling and downstream responses. These include recent advances on temperature regulation at the genomic, transcriptional, post-transcriptional and post-translational levels - directly linking some plant hormone pathways to known thermosensing mechanisms. Where applicable, diverse plant species and various temperature ranges will be presented, along with emerging principles and themes. It is anticipated that a grand unifying synthesis of current and future fundamental outlooks on how fluctuating temperatures regulate important plant hormone signaling pathways can be leveraged towards forward-thinking solutions to develop climate-smart crops amidst our dynamically changing world.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".