The dynamic nature of bud dormancy in trees: environmental control and molecular mechanisms
Bibliographic record
Abstract
In tree species native to temperate and boreal regions, the activity-dormancy cycle is an important adaptive trait both for survival and growth. We discuss recent research on mechanisms controlling the overlapping developmental processes that define the activity-dormancy cycle, including cessation of apical growth, bud development, induction, maintenance and release of dormancy, and bud burst. The cycle involves an extensive reconfiguration of metabolism. Environmental control of the activity-dormancy cycle is based on perception of photoperiodic and temperature signals, reflecting adaptation to prevailing climatic conditions. Several molecular actors for control of growth cessation have been identified, with the CO/FT regulatory network and circadian clock having important coordinating roles in control of growth and dormancy. Other candidate regulators of bud set, dormancy and bud burst have been identified, such as dormancy-associated MADS-box factors, but their exact roles remain to be discovered. Epigenetic mechanisms also appear to factor in control of the activity-dormancy cycle. Despite evidence for gibberellins as negative regulators in growth cessation, and ABA and ethylene in bud formation, understanding of the roles that plant growth regulators play in controlling the activity-dormancy cycle is still very fragmentary. Finally, some of the challenges for further research in bud dormancy are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".