Differences in China Greening Characteristics and its Contribution to Global Greening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it