Recent trends in transcriptional regulation of cold stress tolerance in plants
Bibliographic record
Abstract
Plants are frequently exposed to a wide variety of environmental stressors, including heat, salinity, cold, drought, heavy metals, and other abiotic stresses. These stresses have a significant impact on the productivity, growth, and development of plants. One of the main abiotic stresses that reduces crop productivity is cold (freezing or chilling). Various mechanisms involving altered physiological, biochemical, and molecular processes have been evolved by plants to cope with cold stress. Developments in molecular biology and genetics have produced a number of tools for analyzing the molecular networks underlying a particular trait. Recent developments in genomics have made it easier to comprehend the genetic basis of plants' resistance to cold stress. Being a complex trait, cold stress in plants is governed by more than one gene, including transcription factors that facilitate plants' survival in adverse conditions. In this review, we focus on the state of knowledge on the molecular processes that plants use to adapt to cold stress. There is also discussion of the functions of different transcription factors in plant adaptation and how to use them to enhance crops. Climate variability is expected to increase the frequency and intensity of abiotic stressors like cold, making this research especially relevant to the sustainability of agricultural and food systems. The development of resilient crop varieties and the establishment of sustainable food production, environmental conservation, and rural livelihoods are discussed in this review, which examines the molecular mechanisms, genetic factors, and biotechnological tools involved in cold stress tolerance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".