Nordic Biodiversity Framework : Laying the foundation for Nordic synergy regarding the Global Biodiversity Framework
Bibliographic record
Abstract
Rapid biodiversity loss is one of the most pressing issues of our time. In response, 196 countries, including the Nordic countries, signed the Kunming-Montreal Global Biodiversity Framework (GBF) in 2022. Now the task is to move from agreement to implementation and policy. This challenge requires careful science-based policy grounded in knowledge of local and regional ecosystems and biodiversity challenges, as well as transnational and transdisciplinary collaboration. The Nordic Biodiversity Framework (NBF), a collaborative project between Iceland, Finland, and Denmark, was developed in 2024 to support such synergy. The cultural, political, and historical similarities between the Nordic countries provide good grounds for Nordic cooperation on the protection of nature and biodiversity. However, there are lessons to be learned through comparison. Iceland, Finland, and Denmark have relevant differences in climate, nature, political culture, and infrastructures for biodiversity protection. Both the similarities and differences provide the diversity to create synergy that enables us to learn from each other and identify important issues and leading practices. The NBF aimed to lay the groundwork for this by compiling existing knowledge about biodiversity issues and examining the status of GBF implementation of Targets 1–8 in each of the member countries (Iceland, Finland, and Denmark). To accomplish this, three workshops were conducted in 2024, one in each member country, as well as several supportive side activities. Some of the main differences between NBF member countries are political, primarily in terms of EU membership, which affects policy making, implementation, and governance of biodiversity actions. The threats to biodiversity differ greatly between the three countries but a common thread is competition between biodiversity and business (or defence) interests. Other common themes and messages emerged, such as the need for a better understanding and larger role of the Ecosystem Approach, conflicting interpretations or misunderstandings of key concepts (e.g., biodiversity, protected areas), a lack of effective or relevant policies for biodiversity conservation, and a lack of support and resources. There are different obstacles to implementing the national targets (NBSAPs): Finland's NBSAP has not been approved by the government, Denmark’s NBSAP was criticized by the Danish Biodiversity Council and Iceland failed to submit an updated NBSAP to the Convention of Biological Diversity Secretariat for COP16 in 2024. Regardless of these barriers, there are various efforts ongoing to implement the GBF’s goals and targets in these countries, and there is clearly a general increase of awareness of biodiversity within both the public and political spheres.
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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 | 0.047 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".