Assessment of volatile organic compound emissions from ecosystems of China
Bibliographic record
Abstract
Isoprene, monoterpene, and other volatile organic compound (VOC) emissions from grasslands, shrublands, forests, and peatlands in China were characterized to estimate their regional magnitudes and to compare these emissions with those from landscapes of North America, Europe, and Africa. Ecological and VOC emission sampling was conducted at 52 sites centered in and around major research stations located in seven different regions of China: Inner Mongolia (temperate), Changbai Mountain (boreal‐temperate), Beijing Mountain (temperate), Dinghu Mountain (subtropical), Ailao Mountain (subtropical), Kunming (subtropical), and Xishuangbanna (tropical). Transects were used to sample plant species and growth form composition, leafy (green) biomass, and leaf area in forests representing nearly all the major forest types of China. Leafy biomass was determined using generic algorithms based on tree diameter, canopy structure, and absolute cover. Measurements of VOC emissions were made on 386 of the 541 recorded species using a portable photo‐ionization detector method. For 105 species, VOC emissions were also measured using a flow‐through leaf cuvette sampling/gas chromatography analysis method. Results indicate that isoprene and monoterpene emissions, as well as leafy biomass, vary systematically along gradients of ecological succession in the same manner found in previous studies in the United States, Canada, and Africa. Applying these results to a regional VOC emissions model, we arrive at a value of 21 Tg C for total annual biogenic VOC emissions from China, compared to 5 Tg C of VOCs released annually from anthropogenic sources there. The isoprene and monoterpene emissions are nearly the same as those reported for Europe, which is comparable in size to China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".