Bibliographic record
Abstract
Background & Aim: China has officially established the first batch of national parks and is currently building the world's largest national park system.Achieving unified, standardized and efficient management is an important challenge, and to address this need, this paper analyzes how to build national parks with high quality and optimize governance systems.Review Results: We analyzed the difficulties and problems in the current management model of national parks in China through document analysis and compares the management systems of national parks in the United States, Canada, Australia and Brazil.We found that the management of national parks in China at the central level contains some gaps that may compromise high quality management of system.Currently, the forest resource supervision office (FRSO) is responsible for establishing the management organization of each national park and its post-supervision functions; however, this has drawbacks and the FRSO has difficulty fulfilling its role.In addition, there are problems such as the need to optimize the establishment of the FRSO and in addition, it is at times unclear how to solve management problems of cross-provincial boundary national parks.Some countries with large land areas have established a two-level management system at the central level, for example, the National Park Administration and the regional offices (regional management branch), to promote management on the ground.Talking into account the actual situation of China, the management system of national park should incorporate an optimization plan for implementing the park supervision functions, promote regional management, and define the responsibilities of all parties in the vertical •保护与治理对策•
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".