Swift metabolite changes and leaf shedding are milestones in the acclimation process of grapevine under prolonged water stress
Bibliographic record
Abstract
Grape leaves provide the biochemical substrates for berry development. Thus, understanding the regulation of grapevine leaf metabolism can aid in discerning processes fundamental to fruit development and berry quality. Here, the temporal alterations in leaf metabolism in Merlot grapevine grown under sufficient irrigation and water deficit were monitored from veraison until harvest. The vines mediated water stress gradually and involving multiple strategies: osmotic adjustment, transcript-metabolite alteration and leaf shedding. Initially stomatal conductance and leaf water potential showed a steep decrease together with the induction of stress related metabolism, e.g. up-regulation of proline and GABA metabolism and stress related sugars, and the down-regulation of developmental processes. Later, progressive soil drying was associated with an incremental contribution of Ca 2+ and sucrose to the osmotic adjustment concomitant with the initiation of leaf shedding. Last, towards harvest under progressive stress conditions following leaf shedding, incremental changes in leaf water potential were measured, while the magnitude of perturbation in leaf metabolism lessened. The data present evidence that over time grapevine acclimation to water stress diversifies in temporal responses encompassing the alteration of central metabolism and gene expression, osmotic adjustments and reduction in leaf area. Together these processes mitigate leaf water stress and aid in maintaining the berry-ripening program.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".