Spatial and Temporal Variability of Nitrogen Deposition and Its Impacts on the Carbon Budget of China
Bibliographic record
Abstract
Nitrogen deposition in different regions has different volume and rate. And the impaction of nitrogen deposition is also inconformity on the different ecosystems. In order to study the atmospheric deposition of nitrogen stress on the carbon cycle, we analyzed the history, present and future trends in the evolution of nitrogen deposition, using remote sensing data and models. At the mean while, a series of spatial and temporal nitrogen deposition data was established and install into the Integrated Biosphere Simulator (IBIS), in order to found out the effects of different nitrogen deposition levels on the carbon budget in China. GOME and SCIAMICHY remote sensing data provide us a long time series of nitrogen dioxide column concentration data which can be fitted by the sine function. So it was used to construct nitrogen deposition data associated with ground observation data and recent research results of nitrogen deposition (dry and wet). Along with the nitrogen deposition data, two climate change seniors (A2 and B1) were used to drive the IBIS model. Comparing impact of different nitrogen deposition level, six simulation experiments have been set. The results show that ecosystem responses to nitrogen deposition will be different under future climate change scenarios. In the aggregate, more nitrogen input may not be able to bring more NPP and NEP in the future. At the meanwhile, the responses of different vegetation types to nitrogen deposition will show significant differences.
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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 | 0.001 | 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".