Comparison of Orchid Conservation Between China and Other Countries
Bibliographic record
Abstract
Global attention is highly focused on biodiversity conservation. Various countries are actively implementing relevant conservation measures. To advance these efforts in China, it is essential to understand global conservation actions. The orchid family, one of the most diverse groups of flowering plants, has become a “flagship” group for plant conservation. In this study, we summarized 3418 policies and regulations related to orchid conservation in 45 countries. We found that orchid conservation actions in various countries have focused on in situ conservation, with 1469 policies and regulations issued for nature reserves, while ex situ conservation has been seriously neglected, with only seven relevant regulations. Most developing countries have experienced an increase in orchid conservation actions, while developed countries have plateaued. We amassed 370 non-governmental organizations (NGOs) for orchid conservation. At present, the total number of policies and regulations for orchid protection in China is approximately 84, with 67 issued since 2000. Two non-governmental organizations have been established for orchid conservation. Although the benefit of orchid conservation in China is significant, it still requires continuous improvement compared to many other countries. We recommend that the Chinese government draws on the experiences of the United States, Canada, and Australia in areas such as policy and regulation formulation, optimization of non-governmental organizations, and implementation of related conservation projects. Through learning and collaboration, challenges can be transformed into opportunities for development.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".