Surveying the Hormonome of Hazelnut Catkins During Winter Dormancy
Bibliographic record
Abstract
Abstract Background Deciduous woody perennials, such as hazelnut, undergo winter dormancy to protect sensitive tissues, such as flowers, from harsh conditions. The reproductive success of the tree is dependent on the release of dormancy under favorable conditions. To bloom, the tree must first experience a certain amount of chilling, followed by a certain amount of warmth. With global warming, many trees risk not being able to accumulate enough chilling to release dormancy. Also, when trees accustomed to warm climates are brought into cold climates, they might bloom prematurely at the first sign of spring, and risk freezing damage. The latter is the case for hazelnut, recently adopted as a crop in Ontario, Canada. The present study investigates the hormonal regulation of dormancy in hazelnuts’ male flowers (catkins) by generating hormone profiles in early and late-blooming accessions throughout the dormant season. Abscisic acid (ABA), gibberellin (GA), auxin, cytokinin (CTK), their metabolites, as well as the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC), were measured. Results ABA decreased with dormancy progression, while GA increased. This correlation implies ABA is primarily responsible for dormancy maintenance in catkins and GA works antagonistically to ABA. Indeed, the ABA/GA ratio steadily decreased throughout dormancy. For the first time, CTKs have been reported to steadily increase during dormancy. Auxin and ethylene appear to primarily play a role in the onset of dormancy. Interestingly, early blooming accessions failed to accumulate the auxin conjugate, IAA-Asp and had higher ACC levels throughout most of dormancy. Conclusions Cumulatively, the present study has generated the most comprehensive hormone profile in dormant flowers of deciduous woody perennials and has identified potential strategies for the delay of bloom in hazelnut catkins through the manipulation of hormones.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 0.000 |
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".