Investigating drivers impacting vegetation carbon sequestration capacity on the terrestrial environment in 127 Chinese cities
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
Vegetation cover significantly improves the terrestrial environment by increasing carbon sequestration capacity. It is projected that a major threat to China's terrestrial environment will be happened by 2030 due to the increment in carbon emissions. Identifying reliable techniques to assess carbon absorption by green coverage is necessary to build a resilient environment. This research examines the performance of two weighted regression models to explain the capacity of vegetation carbon sequestration (VCS), spatial distribution, and degree of influence of vegetation coverage for reducing carbon emission. The results demonstrate changes in the VCS capacity from slow to fast, with an average yearly growth rate of 0.043% (2005–2010) to 1.963% (2010–2015) and more obvious growth in local cities. Variables such as the night-time light index, average relative humidity, and length of sunlight substantially impacted VCS capacity, although their effect varied yearly. Finally, the comparative results show that This study can play an influential role in finding specific locations facing issues with carbon emissions and can support local governments through the association of effective measures to mitigate it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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