Diel Variation of Biogenic Volatile Organic Compound Emissions- A field Study in the Sub, Low and High Arctic on the Effect of Temperature and Light
Bibliographic record
Abstract
Many hours of sunlight in the midnight sun period suggest that significant amounts of biogenic volatile organic compounds (BVOCs) may be released from arctic ecosystems during night-time. However, the emissions from these ecosystems are rarely studied and limited to point measurements during daytime. We measured BVOC emissions during 24-hour periods in the field using a push-pull chamber technique and collection of volatiles in adsorbent cartridges followed by analysis with gas chromatography-mass spectrometry. Five different arctic vegetation communities were examined: high arctic heaths dominated by Salix arctica and Cassiope tetragona, low arctic heaths dominated by Salix glauca and Betula nana and a subarctic peatland dominated by the moss Warnstorfia exannulata and the sedge Eriophorum russeolum. We also addressed how climate warming affects the 24-hour emission and how the daytime emissions respond to sudden darkness. The emissions from the high arctic sites were lowest and had a strong diel variation with almost no emissions during night-time. The low arctic sites as well as the subarctic site had a more stable release of BVOCs during the 24-hour period with night-time emissions in the same range as those during the day. These results warn against overlooking the night period when considering arctic emissions. During the day, the quantity of BVOCs and the number of different compounds emitted was higher under ambient light than in darkness. The monoterpenes α-fenchene, α-phellandrene, 3-carene and α-terpinene as well as isoprene were absent in dark measurements during the day. Warming by open top chambers increased the emission rates both in the high and low arctic sites, forewarning higher emissions in a future warmer climate in the Arctic.
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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".