Comparative Assessment of Grassland <scp>NPP</scp> Dynamics in Response to Climate Change in China, North America, Europe and Australia from 1981 to 2010
Bibliographic record
Abstract
Abstract Although climate change has been modifying grassland ecosystems for a long time, few studies on grassland ecosystems have focused on large‐scale responses to climate change. Hence, grassland net primary productivity (NPP) from 1981 to 2010, as well as its variations in China, North America, Europe and Australia, was assessed and compared using a synthetic model in this study. Subsequently, the correlations between the NPP of each grassland type and climate factors were evaluated to reveal the responses of grassland eco‐systems to climate change. The results showed that North America, which has the largest area of grassland ecosystems, exhibits maximum grassland NPP of 4225.30 ± 215.43 Tg DW year−1, whereas Europe, which has the least area of grassland ecosystems among the four regions, exhibits minimum grassland NPP of 928.95 ± 24.68 Tg DW year−1. Grassland NPP presented an increasing trend in China and Australia, but decreasing in Europe and North America from 1981 to 2010. In addition, grassland NPP is positively correlated with mean annual precipitation, but demonstrates notable differences with mean annual temperature. In conclusion, climate change has a significant role in explaining the spatiotemporal patterns of and the variations in grassland NPP in the four regions.
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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".