Safe, sustainable, legal use and trade in wild species: testing a new five-dimensional sustainability assessment
Bibliographic record
Abstract
The Kunming Montreal Global Biodiversity Framework, adopted by the UN Convention on Biological Diversity in 2022, sets ambitious targets to ensure that the use, harvesting and trade of wild species is sustainable, safe and legal. While the definition of 'sustainable' is traditionally inclusive of ecological, social, and economic dimensions, many practically applied standards and regulations often exclude non-ecological perspectives such as human health and animal welfare. Recognising the challenge of assessing sustainability in a comprehensive, but accessible, way, a five-dimensional sustainability assessment framework (5DSAF) was developed, explicitly focusing on social, ecological, economic, animal welfare, and human health dimensions of sustainability. This paper documents the experiences of applying and testing the 5DSAF in multiple species use examples: geographically, by different sectors, and socio-economically. Its application in the United Republic of Tanzania (game meat industry), in South Africa (game meat sector), in Indonesia (reticulated python skins), and in Zimbabwe (Nile crocodile) is discussed. It proposes the steps for the future adaptations, and application of 5DSAF beyond the initial case studies aiming to assist conservation practitioners, policymakers, as well as indigenous peoples and local communities and private sector actors to demonstrate that the use of wild animal species and products is safe, legal and sustainable and, meeting the objectives of One Health approach, and where it is not, to identify the necessary improvements that need to be made.
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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 | 0.048 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".