Atmospheric aerosols elevated ecosystem productivity of a poplar plantation in Beijing, China
Bibliographic record
Abstract
Atmospheric aerosols can influence energy allocation, environmental factors, and thus, canopy photosynthesis. However, the regulations of aerosols on ecosystem productivity are not well understood. Here, we applied the optical properties of aerosols to quantify the effects of aerosol type and concentration on the environmental factors and associated gross primary productivity (GPP) of a poplar (Populus euramericana) plantation during the months of June to August from 2014 to 2016 in Beijing, China. As aerosol optical depth (AOD) increased from 0 to 2.5, total photosynthetically active radiation (PAR) decreased by 29%, while the diffuse PAR increased by 39%. Although there was no significant impact of aerosols on air temperature (p > 0.05), aerosols decreased vapor pressure deficit by more than 40%. We found that the plantation GPP changed exponentially with AOD, indicating that aerosols elevated GPP by about 37% under severe aerosol pollution (AOD ≥ 1) compared with background aerosol (AOD < 0.4). Aerosols type also had a significant effect on GPP. We concluded that aerosols could increase the GPP of the poplar plantation, and the promotion effect of aerosols on poplar plantation would not be significantly reduced until AOD was <1 under the projected decrease in aerosol loading in the future.
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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 | 0.001 | 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".