Identification of environmental stress biomarkers in seedlings of European beech (<i>Fagus sylvatica</i>) and Scots pine (<i>Pinus sylvestris</i>)
Bibliographic record
Abstract
Climate development models predict alterations that will critically influence plant metabolism in southern and central Europe. Although the molecular players involved in the response to climatic stress factors have been well described in crops, little information is available for forest tree species. Consequently, the identification of molecular biomarkers suitable for evaluating the actual impact of different environmental stress conditions on forest plants would be of great importance for monitoring purposes and forest management. In this study, we evaluated a biochemical methodology for the assessment of temperature stress in European beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.) seedlings by analyzing a set of metabolites and enzymes involved in free radical scavenging and cell wall synthesis. The results indicate that the combined analysis of the specific activities and isoform profile of peroxidases, superoxide dismutases, and glutathione peroxidases coupled with the amount variation of phenolic compounds enabled the discrimination between stressed and control seedlings. This approach represents a promising platform for the assessment of temperature stress in forest trees and could also enhance selection and breeding practices, allowing for plants more tolerant and (or) resistant to abiotic stress.
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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".