Effective biodiversity monitoring requires FAIR data and FAIR models for FAIR indicators (Findable, Accessible, Interoperable, and Reusable) [Policy Brief]
Bibliographic record
Abstract
In this policy brief, we highlight the critical role of headline indicators in monitoring progress towards the goals of the Kunming-Montreal Global Biodiversity Framework (GBF). We argue that to enhance the effectiveness of these and other indicators, we need to implement FAIR principles (Findability, Accessibility, Interoperability, and Reusability) across all components involved in indicator calculation—data, workflows, and indicators themselves. Key points include: FAIR Principles: The necessity for all data and methodologies to be openly shared and adhere to FAIR principles to facilitate collaborative monitoring efforts among Parties to the Convention on Biological Diversity (CBD). Standardized Metadata: The importance of guidelines and standardized metadata to ensure consistent usage and interpretation of indicators, with new indicators developed following established standards. Support for Capacity Building: Continuous support is essential for building capacity and developing user-friendly tools to promote broader adoption of indicators across various countries. Implementation Infrastructure: A robust and interconnected data and informatics infrastructure is required to streamline the collection, harmonization, and sharing of biodiversity data and workflows. Policy Recommendations: We call for increased support for data repositories, the development of interconnected infrastructures, adherence to Open and FAIR principles, and the standardization of metadata to improve the usability of indicators. In conclusion, we advocate for a comprehensive and collaborative approach to biodiversity monitoring, emphasizing the need for standardization, transparency, and sustained support to effectively track and report on biodiversity changes globally. Funded by the European Union. B3 (Biodiversity Building Blocks for policy) receives funding from the European Union’s Horizon Europe Research and Innovation Programme (ID No 101059592).
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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 | 0.139 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.036 | 0.048 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.029 | 0.023 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".