Growth and defence of aspen (<i>Populus tremula</i>) after three seasons under elevated temperature and ultraviolet-B radiation
Bibliographic record
Abstract
The predicted climate change will affect the growth and secondary chemistry of plants, but most studies only include leaves and leave out the effects on the secondary chemistry of other plant parts. In a field experiment, we studied the stem phenolic chemistry, as well as the growth of male and female genotypes, of aspen (Populus tremula L.) after three years under single (T, UVA, UVB) and combined (UVA + T, UVB + T) treatments at elevated temperature (T, +2 °C) and ultraviolet-B radiation (+30% UVB). In addition, bud mass and photosynthesis, as well as leaf rust attack and leaf herbivory, were measured. We found that elevated temperature markedly reduced concentrations of salicylates, which were the most abundant compounds among the low relative molecular mass phenolics in the stem. Elevated temperature also increased the biomass, height, and diameter of both males and females, as well as leaf area and herbivory damage in leaves. In combination with UVB, elevated temperature decreased the height of both males and females. Moreover, male buds were bigger than female buds, and females had higher chlorophyll content than males. To conclude, warming promoted growth and reduced phenolic concentration in stems of P. tremula with little difference between the sexes during the adult vegetative phase. UVB had no individual effects on any of the studied variables, implying that the plants were fairly tolerant to increased UVB levels.
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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".