Champions of winter survival: cold acclimation and molecular regulation of cold hardiness in evergreen conifers
Bibliographic record
Abstract
Evergreen conifers are champions of winter survival, based on their remarkable ability to acclimate to cold and develop cold hardiness. Counterintuitively, autumn cold acclimation is triggered not only by exposure to low temperature, but also by a combination of decreasing temperature, decreasing photoperiod and changes in light quality. These environmental cues control a network of signaling pathways that coordinate cold acclimation and cold hardiness in overwintering conifers, leading to cessation of growth, bud dormancy, freezing tolerance and changes in energy metabolism. Advances in genomic, transcriptomic and metabolomic tools for conifers have improved our understanding of how trees sense and respond to changes in temperature and light during cold acclimation and the development of cold hardiness, but there remain considerable gaps deserving further research in conifers. In the first section of this review, we focus on the physiological mechanisms used by evergreen conifers to adjust metabolism seasonally and to protect overwintering tissues against winter stresses. In the second section, we review how perception of low temperature and photoperiod regulate the induction of cold acclimation. Finally, we explore the evolutionary context of cold acclimation in conifers and evaluate challenges imposed on them by changing climate and discuss emerging areas of research in the field.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".