Core targets of Kunming-Montreal Global Framework and recommendations for conservation action in China
Bibliographic record
Abstract
Under the leadership of the Chinese presidency, the second part of the 15th Conference of the Parties to the United Nations Convention on Biological Diversity (CBD) adopted 62 decisions, in particular Kunming-Montreal Global Biodiversity Framework (KM-GBF), which is based on the theory of transformative changes. KM-GBF, its achievements, gaps, and lessons learned, and the experience and achievements of other relevant multilateral environmental agreements, sets out an ambitious plan to implement broad-based action to bring about a transformation in our societies’ relationship with biodiversity by 2030, and draws a new blueprint for global biodiversity governance. This paper provides an interpretation of the three core targets of the framework — the “3030 target” for protected areas, resource mobilisation, and digital sequence information of genetic resources, a brief introduction to the relevant decisions to ensure the implementation of the framework, and recommendations for future conservation actions in China: (1) To strengthen the mainstreaming of biodiversity conservation. Revision of China's Biodiversity Conservation Strategy and Action Plan (2011-2030) is an opportunity to involve the whole government and society in the process and to take action to promote the goals and targets of the KM-GBF; (2) To further develop detailed conservation plans, clarify the scopes, purposes and management measures of conservation areas, and implement responsible authorities and specific measures for implementing the plans. Researches on the Other Effective area-based Conservation Measures (OECMs) are needed to incorporate into the management system for biodiversity conservation; (3) To develop an operational indicator system and monitoring plan in accordance with the monitoring requirements of the framework targets; (4) To continue to strengthen awareness and education on biodiversity conservation, raise public awareness and attention to biodiversity conservation, and promote sustainable production and sustainable consumption in society as a whole; (5) To promote international cooperation vigorously to explore and promote Nature-based Solutions on a larger scale, and find pathways for economic and social development that have positive and beneficial effects on nature.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".