Production of glandular trichomes responds to water stress and temperature in silver birch (<i>Betula pendula</i>) leaves
Bibliographic record
Abstract
Silver birch (Betula pendula Roth) allocates substantial resources to the production of glandular trichomes. If these trichomes can protect trees from temperature and water stress, their production would be expected to increase under these conditions. We studied how glandular trichome density and number in the leaves of two-year-old silver birch plantlets respond to single and combined treatments of elevated temperature (+1 °C) and three different levels of soil moisture (low, normal, and excess watering). Moreover, we quantified the seasonal variation in trichome density in mature long-shoot leaves of young, greenhouse-grown silver birches. Our results demonstrate clear differences between responses of glandular trichomes on different leaf surfaces. On the adaxial leaf surface, both drought and elevated temperature reduced the production of glandular trichomes. Interestingly, this response was absent in plants subjected to the combined treatment. Glandular trichome production on the abaxial leaf surface increased considerably in leaves produced during the growing season, reflecting a seasonal trend. Maintaining a strong seasonal increase in trichome production of abaxial surfaces even in low-water conditions suggests an important, though still unknown, role for abaxial glandular trichomes. In silver birch stems, those trichomes are strongly responsible for herbivore defense.
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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 | 0.000 |
| 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".