Evaluation of the Carbon Sink Capacity of the Proposed Kunlun Mountain National Park
Bibliographic record
Abstract
National parks, as an important type of nature protected areas, are the cornerstone that can effectively maintain biodiversity and mitigate global climate change. At present, China is making every effort to build a nature-protection system, with national parks as the main body, and this approach considers China's urgent goals of obtaining carbon neutrality and mitigating climate change. It is of great significance to the national carbon-neutralization strategy to accurately predict the carbon sink capacity of national park ecosystems under the background of global change. To evaluate and predict the dynamics of the carbon sink capacity of national parks under climate change and different management measures, we combined remote-sensing observations, model simulations and scenario analyses to simulate the change in the carbon sink capacity of the proposed Kunlun Mountain National Park ecosystem over the past two decades (2000-2020) and the change in the carbon sink capacity under different zoning controls and various climate change scenarios from 2020 to 2060. Our results show that the carbon sink capacity of the proposed Kunlun Mountain National Park area is increasing. Simultaneously, the carbon sink capacity will be improved with the implementation of park management and control measures; which will be increased by 2.04% to 2.13% by 2060 in the research area under multiple climate change scenarios. The research results provide a scientific basis for the establishment and final boundary determination of the proposed Kunlun Mountain National Park.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".