Forest ecological security in China: A quantitative analysis of twenty five years
Bibliographic record
Abstract
Forest Ecological Security (FES) is an essential component of ecological security and plays an important role in national security. In this study, the Delphi method was used to construct a multi-index comprehensive evaluation system, and indicators were weighted using the entropy weight method. Based on data from 31 provinces, this study evaluated the status of FES in China from 1994 to 2018 and identified the influencing factors of FES. The results showed that indicators such as forest stock volume per unit land area, forest coverage rate, population density, and population per unit forest area contributed the most to the evaluation indicator system. Forest quantity and quality, as well as population pressure, were especially important factors affecting FES. In terms of spatial distribution, FES varied greatly between regions and the developments were unbalanced. Provinces in the northeast, southern and southwest forest regions, such as Jilin, Heilongjiang, Fujian, Jiangxi, Yunnan, etc., had better FES due to their rich forest resources. The provinces in northwest China (Xinjiang, Qinghai, Gansu, and Ningxia) and north China (Shanxi, Hebei, Beijing, and Tianjin) had worse FES than other provinces. With the reference of temporal variation, the national average FES increased from 0.543 (1994–1998) to 0.593 (2014–2018). Furthermore, 51.6% of the provinces (Hainan, Fujian, Heilongjiang, Beijing, etc.) had continuous upward trends in their evaluation value of FES (VFES) during the study period, while the other provinces (Inner Mongolia, Shanghai, Shandong, etc.) exhibited fluctuating VFES. In general, the VFES of all provinces increased during the whole study period, and the average level of FES improved significantly, which indicated that China’s FES situation has shown continued improvement.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".