Compensatory mechanisms mitigate the effect of warming and drought on wood formation
Bibliographic record
Abstract
Because of global warming, high-latitude ecosystems are expected to experience increases in temperature and drought events. Wood formation will have to adjust to these new climatic constraints to maintain tree mechanical stability and long-distance water transport. The aim of this study is to understand the dynamic processes involved in wood formation under warming and drought. Xylogenesis, gas exchange, water relations and wood anatomy of black spruce [Picea mariana (Mill.) B.S.P.] saplings were monitored during a greenhouse experiment where temperature was increased during daytime or night-time (+6 °C) combined with a drought period. The kinetics of tracheid development expressed as rate and duration of the xylogenesis sub-processes were quantified using generalized additive models. Drought and warming had a strong influence on cell production, but little effect on wood anatomy. The increase in cell production rate under warmer temperatures, and especially during the night-time warming at the end of the growing season, resulted in wider tree-rings. However, the strong compensation between rates and durations of cell differentiation processes mitigates warming and drought effects on tree-ring structure. Our results allowed quantification of how wood formation kinetics is regulated when water and heat stress increase, allowing trees to adapt to future environmental conditions.
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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 | 0.001 |
| 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".