Quantifying the Interaction Effects of Climatic Factors on Vegetation Growth in Southwest China
Bibliographic record
Abstract
Due to the complex and variable climate structure in Southwest China (SW), the impacts of climate variables on vegetation change and the interactions between climate factors remain controversial, considering the uncertainty and complexity in the relationships between climate factors and vegetation in this region. In this study, the CRU TS v. 4.02 from 1982 to 2017 and the annual maximum (P100), upper quarter quantile (P75), median (P50), lower quarter quantile (P25), minimum (P5), and mean (Mean) of GIMMS NDVI were utilized to reveal the main and interaction effects of significant climate variables on vegetation development at the level of SW and the core areas (CAs) of typical climate type (including T+ *–P+ *, T+ *–P–, T+ *–P+, and NSC) using the simple moving average method, a multivariate linear model, the slope method, and the Johnson–Neyman method. The obtained regression relationships between NDVI, temperature, and precipitation were verified successfully by constructing multiple linear models with interaction terms. Within the T+ *–P– CA, precipitation had the main impact; meanwhile, in the SW and other CAs, the temperature had the main effect. In general, most of the significant moderating effects of temperature (precipitation) on vegetation growth predominantly increased with the increase in precipitation (temperature). Nevertheless, the significant moderating effect varied in different regions and directions. In the SW area, when the temperature/precipitation was in the range of [4.73 °C, 5.13 °C]/[730.00 mm, 753.95 mm], the impact of temperature/precipitation on NDVI had a significant positive regulating effect with respect to the precipitation/temperature. Meanwhile, in the NSC/T+ *–P+ * areas, when the temperature/precipitation was in the range of [15.99 °C, 16.03 °C]/[725.17 mm, 752.82 mm], the impact of temperature/precipitation on NDVI has a significant negative moderating role with respect to the precipitation/temperature. Overall, our study provides a modern context for clearly uncovering the complexity of the effect of climate alteration on vegetation development, allowing for clarification of the alterations in vegetation development due to climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".