Coverage and distribution of national key protected wild species in China’s nature reserves
Bibliographic record
Abstract
Aims: Nature reserves are essential venues for the conservation of wild flora and fauna, and the coverage of species protection by nature reserves has always been a focal point.Methods: This study utilized GBIF data, literature sources, and scientific research data from nature reserves, drawing on the List of National Key Protected Wild Animals (2021 Edition) and the List of National Key Protected Wild Plants (2021 Edition), to analyze the coverage and distribution of national key protected wild species within nature reserves in China.Results: The results indicate that 78.12% of the national key protected wild species are conserved within nature reserves.Specifically, the coverage for key protected wild animals is found to reach 85.58%, while for key protected wild plants, it is 71.95%.The spatial density distribution of wild species within nature reserves is uneven, displaying a •研究报告• 郭雨桐等: 全国自然保护地对国家重点保护野生物种的覆盖度及其分布状况 2025年 | 33卷 | 3期 | 24423 | 第2页decreasing trend from southwest to northeast.At the family level, the highest protection coverage is observed for Orchidaceae, followed by Fabaceae, Lycopodiaceae, and Pinaceae.In terms of provincial distribution, Yunnan, Sichuan, and Guangdong have the highest number of national key protected wild species.Conclusion: Overall, the majority of national key protected wild species are effectively conserved within nature reserves, and nature reserves play an important role in protecting biodiversity in China and fulfilling the commitments under the Kunming-Montreal Global Biodiversity Framework.
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.
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 | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".