Metrics for biodiversity and health policy integration
Bibliographic record
Abstract
Despite over a decade of progressive commitments from parties to the Convention on Biological Diversity (CBD), integrated biodiversity and health indicators and monitoring mechanisms remain limited, hampering achievement of the sustainable development goals and improvements in health and well-being. Adoption of the Kunming-Montreal Global Biodiversity Framework (2022) and the Global Action Plan on Biodiversity and Health (2024) provide a renewed entry point to shape the way governments approach health and wellbeing and address the environmental burden of disease. This is a critical opportunity that scholars at the health-environment nexus should not miss. This Perspective outlines building blocks to mobilize the field, starting with essential terminology and a scope of metrics needed by governments. We then evaluate elements to be considered in the construction of integrated metrics, including concepts, overarching challenges, a review of scientific hypotheses from an ecological perspective, as well as a set of principles and characteristics for indicators. To raise awareness across parallel communities of practice working at the health-environment nexus, we then briefly examine four approaches to integrated metrics developed by: conservationists, Indigenous scholars, One Health experts, and planetary health experts. We conclude with actionable steps to enhance governance, mobilize funding, and apply integrated indicators in national and global strategies. A broad science community is needed to support national governments to meet global commitments to address biodiversity loss and the environmental burden of disease concurrently. The overall aim of this paper is to contribute to addressing biodiversity loss by effectively linking policy and transdisciplinary practice at the health-environment nexus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".