Global Biodiversity Framework Action Plan
Bibliographic record
Abstract
The document describes how Biodiversa+, the European Biodiversity Partnership, will support the implementation of the Kunming-Montreal Global Biodiversity Framework (GBF). The GBF was adopted in 2022 by 196 parties to the CBD and is considered a key global roadmap for halting biodiversity loss by 2030 and achieving the vision of ‘living in harmony with nature’ by 2050. It comprises four overarching goals and 23 specific, mostly quantitative targets (including the “30x30” conservation target, reducing pesticide risks, phasing out environmentally harmful subsidies, and mobilising $200 billion annually for biodiversity). Implementation occurs through national biodiversity strategies (NBSAPs), supported by a comprehensive monitoring and reporting system. Biodiversa+ can play a key role in enabling evidence-based implementation of the GBF across Europe by networking and strengthening research, monitoring, innovation, policy advice, and international cooperation. This Action Plan focuses on three strategic areas of support: Mainstreaming & capacity building Knowledge development & exchange Monitoring & reporting Biodiversa+ can play a key role in the science-based implementation of the GBF in Europe through research, harmonised monitoring, knowledge transfer, capacity building and close cooperation with international and regional actors.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.017 |
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".