Do volatile organic compound emissions of Tunisian cork oak populations originating from contrasting climatic conditions differ in their responses to summer drought?
Bibliographic record
Abstract
The intrinsic variability of volatile organic compound emissions and photosynthetic parameters in response to two drying cycles was investigated on Quercus suber L. saplings originating from three Tunisian populations native to contrasting climates. Emissions mainly included monoterpenes plus traces of sesquiterpenes and methylsalicylate that steadily decreased during the experiment unrelated to treatments. Instead, monoterpene emissions increased by 30% during a first moderate drought and remained enhanced after rewatering, while CO2–H2O gas exchange was reduced by 30%. A second severe drought decreased monoterpene emissions to about two third of controls, strongly inhibited gas exchange and photochemical efficiency, and caused a partial loss of chlorophyll. Rewatering led to only partial recovery of emissions and photosynthetic parameters of stressed plants whose biomasses and leaf nitrogen and chlorophyll contents were lower than in control plants. There was no clear difference among populations in volatile organic compound emissions or photosynthetic parameters. However, the population inhabiting the most arid region had intrinsically smaller leaves with lower specific leaf masses and higher drought-related leaf losses than populations native to less arid climates. These results indicate an adaptive shift from stress-tolerating towards stress-avoiding ecotypes under arid climates involving mainly morphological adaptations without apparent differentiation in volatile organic compound production, probably because of its high phenological plasticity.
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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) | 0.000 | 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".