Transformational changes for achieving the Post-2020 Global Biodiversity Framework ecological connectivity goals
Bibliographic record
Abstract
The first draft of the United Nations Convention on Biological Diversity (CBD) Post-2020 Global Biodiversity Framework (GBF) includes an unprecedented call for states that have ratified the treaty (Parties) to implement measures to maintain and enhance ecological connectivity as urgent actions to abate further biodiversity loss and ecosystem decline. Considering the challenges that lie ahead for Parties to the CBD, we highlight the ways in which effective and equitable connectivity conservation can be achieved through four transformative changes, including: (1) mainstreaming connectivity retention and restoration within biodiversity conservation sector and influencing sectors (e.g., transportation, energy, agriculture, forestry); (2) mainstreaming financial resources and incentives to support effective implementation; (3) fostering collaboration with a focus on cross-sector collective action; and (4) investing in diverse forms of knowledge (co-)production and management in support of adaptive governance. We detail 15 key actions that can be used to support the implementation of these transformative changes. While ambitious, the transformative changes and associated key actions recommended in this perspective will need to be put in place with unprecedented urgency, coherency, and coordination if Parties to the CBD truly aspire to achieve the goals and targets of the forthcoming Post-2020 GBF in this new decade of biodiversity.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".