Doubled volatile organic compound emissions from subarctic tundra under simulated climate warming
Bibliographic record
Abstract
*Biogenic volatile organic compound (BVOC) emissions from arctic ecosystems are important in view of their role in global atmospheric chemistry and unknown feedbacks to global warming. These cold ecosystems are hotspots of climate warming, which will be more severe here than averaged over the globe. We assess the effects of climatic warming on non-methane BVOC emissions from a subarctic heath. *We performed ecosystem-based chamber measurements and gas chromatography-mass spectrometry (GC-MS) analyses of the BVOCs collected on adsorbent over two growing seasons at a wet subarctic tundra heath hosting a long-term warming and mountain birch (Betula pubescens ssp. czerepanovii) litter addition experiment. *The relatively low emissions of monoterpenes and sesquiterpenes were doubled in response to an air temperature increment of only 1.9-2.5 degrees C, while litter addition had a minor influence. BVOC emissions were seasonal, and warming combined with litter addition triggered emissions of specific compounds. *The unexpectedly high rate of release of BVOCs measured in this conservative warming scenario is far above the estimates produced by the current models, which underlines the importance of a focus on BVOC emissions during climate change. The observed changes have implications for ecological interactions and feedback effects on climate change via impacts on aerosol formation and indirect greenhouse effects.
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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".