Release of biogenic volatile organic compounds and physiological responses of two sub-tropical tree species to smoke derived from forest fire
Bibliographic record
Abstract
Forests emit a large amount of biogenic volatile organic compounds (BVOCs) in response to biotic and abiotic stress. Despite frequent occurrence of large forest fires in recent years, the impact of smoke stress derived from these forest fires on the emission of BVOCs is largely unexplored. Thus, the aims of the study were to quantify the amount and composition of BVOCs released by two sub-tropical tree species, Cunninghamia lanceolata and Schima superba, in response to exposure to smoke. Physiological responses and their relationship with BVOCs were also investigated. The results showed that smoke treatments significantly (p < 0.001) promoted short-term release of BVOCs by C. lanceolata leaves than S. superba; and alkanes, olefins and benzene homologs were identified as major classes of BVOCs. Both C. lanceolata and S. superba seedlings showed significant (p < 0.005) physiological responses after being smoke-stressed where photosynthetic rate remained unaffected, chlorophyll content greatly reduced and Activities of anti-oxidant enzymes and the malondialdehyde content generally increased with the increase in smoke concentration. Activities of anti-oxidant enzymes showed mainly positive correlations with the major BVOCs. In conclusion, the release of BVOCs following smoke stress is species-specific and there exists a link between activities of antioxidant enzymes and BVOCs released. The findings provide insight about management of forest fires in order to control excessive emission of smoke that would trigger increased release of BVOCs.
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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".