Huge variations in the terpene emission potentials of Norway spruce
Bibliographic record
Abstract
Norway Spruce dominates Europe’s Boreal and sub-Alpine coniferous forests and is widely planted in southeastern Canada, in the northeastern United States, in the Rocky Mountains, and in the Pacific Coast region. Therefore it is expected to have significant contribution to emissions of biogenic volatile organic compounds. There are studies on isoprene, monoterpene and sesquiterpene emissions of Norway spruce, but emission potentials and composition vary a lot. Especially the emission potentials of sesquiterpenes are highly varying. When considering the atmospheric impact of biogenic emissions, it is essential to know their composition since, for example, aerosol formation potential of different compounds varies a lot. In this study, we compiled all available data for Norway spruce emissions that could be used for emission potential calculations of terpenes and searched if growing location, season, or age of the tree could explain the variations. In addition to previously published data, we included new data from a boreal forest in southern Finland from 2019 and 2021 and a sub-Arctic boreal forest in northern Finland from 2020. The differences between various trees and sites were huge, but variations of emission potentials, especially for sesquiterpenes, over the growing season were also high, even for the same tree. Emission potentials of isoprene, monoterpenes and sesquiterpenes varied between 5 – 2500, 25 – 3200 and 42 – 15000 ng g(dw)-1 h-1, respectively. No clear trend could be observed on the basis of our measurements and other relevant studies. However, there were indication of some phenomena, e.g., none of the young trees emitted isoprene while some older trees had huge isoprene emissions. It is also noteworthy that sesquiterpene emission rates greatly increase at the end of a growing season.
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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 | 0.001 | 0.001 |
| 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".