Leveraging AI for enhanced alignment of national biodiversity targets with the global biodiversity goals
Bibliographic record
Abstract
• Environmental Concerns: Our research showcases AI's potential to enhance the alignment between national and global biodiversity targets. By facilitating the identification of areas needing improvement, such as biosafety and gender equality, we identify a pathway for more effective conservation efforts and policy revisions to achieve global biodiversity goals. • Economic Concerns: The AI-driven analysis identifies gaps in integrating biodiversity considerations within the business sector as well as pinpoints areas of lesser alignment, which aids in formulating a targeted strategy for resource allocation to support a more impactful contribution towards global biodiversity objectives. • Social Concerns: By pinpointing underrepresented areas such as gender equality and indigenous peoples' rights, our research advocates for NBSAP revisions to be both inclusive and comprehensive. This ensures that biodiversity conservation efforts are equitable, supporting societal well-being and sustainable development by integrating diverse voices and knowledge systems. This research explores the innovative application of artificial intelligence (AI), specifically OpenAI's GPT-3.5 model, in assessing the alignment between National Biodiversity Targets (NBTs) and the Kunming-Montreal Global Biodiversity Framework (GBF). Addressing biodiversity loss requires aligning national efforts with global objectives, a complex task due to the vast amount of biodiversity data and the diversity of biodiversity strategies across countries. By leveraging AI, this study introduces a scalable, efficient method to evaluate the congruence between 599 NBTs from 26 countries and the GBF goals and targets. Our methodology combines traditional natural language processing techniques with large language model insights utilizing GPT-3.5 to examine the similarity between national and global biodiversity targets and identify recommendations to enhance target alignment. The study achieves two main objectives: 1) providing actionable insights for countries to accelerate alignment with the GBF through their National Biodiversity Strategy and Action Plan (NBSAP) Target Similarity Assessments, and 2) mapping the global landscape of biodiversity policy alignment to inform strategic planning for the 16th Biodiversity Conference of Parties (COP16). The analysis reveals strong alignment with GBF Goals A and B, as well as Targets 4, 10, and 14, while highlighting areas for improvement in gender equality, biosafety, and business sector engagement. This research demonstrates AI's capacity to streamline biodiversity policy alignment, offering specific guidance for nations to refine their biodiversity strategies. The study underscores the importance of human-centered, transparent AI applications in supporting global biodiversity goals, advocating for collaborative, multi-sectoral efforts to enhance policy coherence and achieve the ambitious objectives of the 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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".