Evaluation of Vegetation Responses to Climatic Factors and Global Vegetation Trends using GLASS LAI from 1982 to 2010
Bibliographic record
Abstract
Vegetation growth has been profoundly affected by global climate change. It is important to investigate vegetation responses to climatic factors and vegetation trends with remote sensing data. In this study, we explored the responses of global vegetation to 3 climatic factors (temperature, precipitation, and solar radiation) and global vegetation trends based on the Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) dataset from 1982 to 2010. The main findings are: (i) the vegetation responses to temperature and precipitation have no apparent time-lag and the vegetation response to the solar radiation has a time-lag of 1 month in most places in the northern hemisphere; (ii) the driving factor of the growth of vegetation was air temperature, followed by precipitation and solar radiation; (iii) the closest relationships between vegetation and climatic factors were observed in mixed forest, deciduous needleleaf forest, and shrublands at northern mid- and high-latitude; (iv) a map of global vegetation trends from 1982 to 2010 was derived, and showed that the proportion of vegetated pixels with significant increasing and decreasing trends was 34.73% and 6.59% (p < 0.05), respectively; (v) more reasonable vegetation trends can be obtained from the GLASS LAI dataset in the perennial cloud-covered 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 | 0.001 |
| 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".