Vegetation inter-annual variation responses to climate variation in different geomorphic zones of the Yangtze River Basin, China
Bibliographic record
Abstract
Despite numerous studies on the response of vegetation change to climate variation, they have mainly been based on annual mean temperature and annual mean precipitation, and have failed to reveal the impact of climate on the inter-annual variation of vegetation in different geomorphic zones based on non-linear methods and considering daily low and high temperature. In this study, the effect of climate variation on the inter-annual variation of vegetation in different geomorphic zones along the area of t the Yangtze River Basin, China from 1982 to 2015 is revealed using Ensemble Empirical Mode Decomposition method. The results show that the inter-annual variation of vegetation is frequently fluctuating, mainly on a short time scale (3-year time scale), and its contribution gradually increases from 62.35% to 73.57% with increasing relief (from plain to high undulating mountain). The vegetation change of more than 75% of the areas is dominated by inter-annual variations, moreover, the vegetation change of the low relief areas is dominated by the 3-year timescale along with the long-term trend, while that of the other geomorphic zones is dominated by the 3-year timescale only. Inter-annual vegetation variation is positively related to precipitation and maximum temperature, but negatively related to mean temperature and minimum temperature in most areas, with area percentages of 67.20%, 86.55%, 65.38% and 75.02%. Inter-annual variation is mainly controlled by maximum temperature in most areas (54.77%). These results will deepen our understanding of the vegetation-climate relationship, suggesting that the responses of inter-annual variation in vegetation to climate vary not only on different time scales, but also in indifferent geomorphic zones.
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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".