Differences in China Greening Characteristics and its Contribution to Global Greening
Bibliographic record
Abstract
With the rapid emergence of the global greening phenomenon under remote sensing monitoring, the prevailing trend of phenomenon analysis and traceability research is self-evident. However, identifying characteristics is basic research of the greening phenomenon, which sometimes subverts research results. The choice of method may directly affect the difference in the greening-browning range, which is easily overlooked. At the same time, influenced by the regional vegetation state’s basic value, the greening contribution’s spatialization still needs to be further verified. Based on the enhanced vegetation index results at the global kilometer-grid scale, this research chose to use the maximum value composite and the simple average method to explore the differences in China’s characteristic identification process initially. While paying attention to results and phenomena, scholars’ attention to basic research needs further improvement. The results show that the widely used two groups of basic methods have shown noticeable differences in greening and browning, and are affected by human activities, climate, geographical environment, etc. And this directional error and the phenomenon of hasty generalization are the most easily ignored in much basic research. The vegetation information considering the inherent stock and changing flux has quantified the greening contribution between regions. China, Brazil, and India dominate global greening, and Canada significantly contributes to browning. Some regions must promote the greening trend of changing flux while maintaining the inherent stock advantage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".