Analysis of the main elements and implications of the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
Background & Aim: The Conference of the Parties to the Convention on Biological Diversity adopted a new global biodiversity strategy-the Kunming-Montreal Global Biodiversity Framework (the Kunming-Montreal GBF). The following five package outcomes were also adopted: Monitoring Framework; Mechanisms for Planning, Monitoring, Reporting and Review; Resource Mobilization; Capacity-Building and Development, Scientific and Technological Cooperation; and Genetic Resources Digital Sequence Information. Parties to the Convention need to implement the Kunming-Montreal GBF and its package of outcomes domestically. In order to facilitate China's implementation of the Kunming-Montreal GBF, the paper briefly reviews the development process and the main contents of the Kunming-Montreal GBF and its package of outcomes. Review Results: Comments on the global and national impact of the Kunming-Montreal GBF are given. In order to implement the Kunming-Montreal GBF, developed countries should enhance biodiversity financing and ensure the level of international funds related to biodiversity flowing to developing countries. Developing countries should revise or update national biodiversity strategies and action plans, monitor and report national implementation progress by using indicators, and accept review. China should host the inter-sessional process of the CBD and facilitate the implementation of the package deals of the Kunming-Montreal GBF.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".