Ecosystem services “on the move” as a nature-based solution for financing the Global Biodiversity Framework
Bibliographic record
Abstract
Abstract The Kunming-Montreal Global Biodiversity Framework (GBF) aims to halt global biodiversity loss. However, its implementation process will need strategic financing particularly to address the divide between the Global North and Global South. Highly migratory marine vertebrates (henceforth marine megafauna) connect distant ecosystems providing ecosystem services across jurisdictions with considerably different conservation interests and economic ability to pay for biodiversity protection. Although such migratory behaviour presents a specially challenging case for protection, because it provides a direct link between developed and less-developed countries it can provide a key to unlock the potential for financial support for implementing the GBF and shed light on a nature-based solution for how Official Development Assistance (ODA) could be deployed. Such ODA could ensure the global protection of these charismatic and threatened species, while contributing to the financing of the GBF. Our work emphasises the economic value of marine megafauna ecosystem services provided “on the move” across jurisdictions and highlights the economic value of conserving marine megafauna, our global heritage.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".