Impacts of climate change and human activities on vegetation cover changes in the Fen River headwaters based on long time series data
Bibliographic record
Abstract
As an indicator of climate change, the change of vegetation cover directly reflects the ecosystem dynamics of the region.In this paper, the spatial and temporal characteristics of vegetation cover in the headwaters of the Fen River and the effects of temperature, precipitation, GDP and population on the changes of vegetation cover were statistically analyzed by using the Theil-Sen median slope and the Mann-Kendall test and Pearson's correlation coefficient from 2000 to 2020.The results showed that: (1) from 2000 to 2020, the vegetation cover of the Fen River headwaters showed an overall upward trend, and the mean value of NDVI was 0.55.The fluctuation increased from 2000 to 2011; the significant increase was observed from 2011 to 2013; and the fluctuation of the value of NDVI from 2013 to 2020 was relatively small 0.01 p .(2) Climate change affects changes in vegetation cover.On the time scale, the 2000-2020 mean NDVI values are positively correlated with temperature and precipitation, but the correlation is not significant 0.053 0.05, 0.185 0.05 p p . On the spatial scale, vegetation cover was weakly negatively correlated with air temperature as a whole, while positively correlated with precipitation as a whole.(3) The influence of human activities on vegetation cover was dominant, NDVI and GDP were positively correlated, with only 5.13% negatively correlated in the central and northeastern part of the region, and NDVI and population were strongly positively and negatively correlated, with alternating distribution in the study area.(4) The vegetation cover of the Fen River headwaters area shows an increasing trend, but there are still ecological and environmental problems, and it is necessary to continue to improve the implementation of the relevant ecological protection policies in order to achieve the goal of sustainable development.The results of the study can provide scientific references for the restoration of vegetation cover and protection of fragile ecosystems in the transition zone of semi-arid and semi-humid climate.
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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) | 0.001 | 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".