Cold Acclimation and Freezing Tolerance in Plants
Bibliographic record
Abstract
Abstract The capacity to survive winter conditions varies greatly in the plant kingdom. Cold acclimation is the process leading to the development of freezing tolerance in plants. It is a complex multigenic process that requires a programmed and integrated genetic capacity to activate the appropriate mechanisms needed to withstand harsh winter conditions. Hardy plants have evolved complex mechanisms to tightly regulate gene expression, including events at the transcriptional and post‐transcriptional levels. Hundreds of cold‐induced genes encoding structural and regulatory proteins have been identified. These proteins have been found in many species, but in most cases, they had been first identified in model species. Most of the studies in the field are still performed with the model dicotyledonous plant Arabidopsis , but Brachypodium distachyon is emerging as a model for monocotyledonous species. Key Concepts: Plants must possess the genetic makeup to develop tolerance to harsh winter conditions. Low temperature induces the expression of many genes in plants. The C‐repeat binding factor (CBF) pathway is still the only identified pathway that regulates gene expression at low temperature. The existence of the CBF components in a species does not ensure the capacity to cold acclimate. Although microRNAs associated with cold response have been discovered, few have been assigned to specific physiological events in the cold acclimation process. Few links between the regulation of cold stress response and chromatin dynamics have been identified in plants thus far. Brachypodium distachyon is a potential model for the study of cold acclimation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".