Phenolic metabolites in the resistance of northern forest trees to pathogens — past experiences and future prospects
Bibliographic record
Abstract
Phenolic metabolites are frequently implicated in chemical defense mechanisms against pathogens in woody plants. However, tree breeding programmes for resistance to pathogens and practical tree-protection applications based on these compounds seem to be scarce. To identify gaps in our current knowledge of this subject, we explored some of the recent literature on the involvement of phenolic metabolites in the resistance of northern forest trees (Pinus, Picea, Betula, Populus, and Salix spp.) to pathogens. Although it is evident that the phenolic metabolism of trees is often activated by pathogen attacks, few studies have convincingly established that this induction is due to a specific defense response that is capable of stopping the invading pathogen. The role of constitutive phenolics in the resistance of trees to pathogens has also remained unclear. In future studies, the importance of phenolics in oxidative stress, cell homeostasis and tolerance, and the spatial and temporal localization of phenolics in relation to invading pathogens should be more carefully acknowledged. Possibilities for future studies using advanced methods (e.g., metabolic profiling, confocal laser scanning microscopy, and use of modified tree genotypes) are discussed.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".