Volatile organic compound emissions in relation to plant carbon fixation and the terrestrial carbon budget
Bibliographic record
Abstract
A substantial amount of carbon is emitted by terrestrial vegetation as biogenic volatile organic compounds (VOC), which contributes to the oxidative capacity of the atmosphere, to particle production and to the carbon cycle. With regard to the carbon budget of the terrestrial biosphere, a release of these carbon compounds is regarded as a loss of photosynthetically fixed carbon. The significance of this loss for the regional and global carbon cycles is controversial. We estimate the amount of VOC carbon emitted in relation to the CO2 taken up, based on our own enclosure and micrometeorological flux measurements of VOC emissions and CO2 exchange within the Mediterranean area and the tropical rainforest in Amazonia and on literature data. While VOC flux estimates are small in relation to net primary productivity and gross primary productivity, the amount of carbon lost as VOC emissions can be highly significant relative to net ecosystem productivity. In fact, VOC losses are of the same order of magnitude as net biome productivity. Although we must assume that large amounts of these reemissions are recycled within the biosphere, a substantial part can be assumed to be lost into longer‐lived oxidation products that are lost from the terrestrial biosphere by transport. However, our current knowledge does not allow a reliable estimation of this carbon loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".