Cold Hardiness in Trees: A Mini-Review
Bibliographic record
Abstract
Significant advances have been made in our understanding of the regulation of cold hardiness. The existence of numerous biophysical and biochemical adaptive mechanisms in perennial woody plants and the complexity their regulation has made the development of methods for managing and improving cold hardiness in perennial woody plants has been very difficult. This may be partially attributed to viewing cold hardiness as a single dimensional response, rather than as a complex phenomenon, involving different mechanisms (avoidance and tolerance), different stages (mid-winter vs. late winter), and having an intimate overlap with the genetic regulation of dormancy. In particular separating the molecular regulation of cold hardiness from growth processes has been challenging. ICE and C-repeat binding factor (CBF), transcription factors (Inducer of CBF expression and CRT-binding factor) have been shown to be an important aspect in the regulation of cold-induced gene expression. Evidence has emerged, however, that they are also intimately involved in the regulation of growth, flowering, dormancy, and stomatal development. This evidence includes the presence of CBF binding motifs in genes regulating these processes, or through cross-talk between the pathways that regulate them. Recent changes in climate that have resulted in erratic episodes of unseasonal warming followed by more seasonal patterns of low temperatures has also highlighted the need to better understand the genetic and molecular regulation of deacclimation, a topic of research that is only more recently being addressed. Environmentally-induced epigenetic regulation of stress responses and seasonal processes such as cold acclimation, deacclimation, and dormancy have been documented but are still poorly understood. Advances in the ability to efficiently generate large DNA and RNA datasets and genetic transformation technologies have greatly increased our ability to explore the regulation of gene expression and explore genetic diversity. Greater knowledge of the interplay between epigenetic and genetic regulation of cold hardiness, along with the application of advanced genetic analyses, such as genome-wide-association-studies (GWAS), are needed to develop strategies for addressing the complex processes associated with cold hardiness in woody plants. A cautionary note is also indicated regarding the time-scale needed to examine and interpret plant response to freezing temperatures if progress is to be made in developing effective approaches for manipulating and improving cold hardiness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".